Repository: comic/evalutils (language: python)

Issue:
Remove `distance_transform_edt_float32`
This is no longer necessary now that we run on AWS.


Discussion / hints:

Fix patch:
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 01c73ce..f57af64 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -8,7 +8,7 @@ on:
       - published
 
 env:
-  MINIMUM_PYTHON_VERSION: "3.8"
+  MINIMUM_PYTHON_VERSION: "3.9"
 
 concurrency:
   group: ${{ github.head_ref || github.run_id }}
@@ -36,7 +36,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
     steps:
       - uses: actions/checkout@v3
       - name: Setup Python ${{ matrix.python-version }}
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 9fb0648..7448ef0 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.6.0
     hooks:
       - id: check-docstring-first
       - id: debug-statements
@@ -8,22 +8,22 @@ repos:
       - id: mixed-line-ending
       - id: trailing-whitespace
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.3.1
+    rev: v3.17.0
     hooks:
       - id: pyupgrade
         language: python
-        args: [--py38-plus]
+        args: [--py39-plus]
   - repo: https://github.com/pycqa/isort
-    rev: 5.12.0
+    rev: 5.13.2
     hooks:
       - id: isort
   - repo: https://github.com/ambv/black
-    rev: 23.1.0
+    rev: 24.8.0
     hooks:
       - id: black
         language: python
   - repo: https://github.com/pycqa/flake8
-    rev: 6.0.0
+    rev: 7.1.1
     hooks:
       - id: flake8
         language: python
@@ -35,6 +35,6 @@ repos:
           - mccabe
           - yesqa
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: 'v1.0.1'
+    rev: 'v1.11.1'
     hooks:
       - id: mypy
diff --git a/HISTORY.md b/HISTORY.md
index 622bc9c..0db72ab 100644
--- a/HISTORY.md
+++ b/HISTORY.md
@@ -1,5 +1,11 @@
 # History
 
+## 0.5.0 (2024-08-07)
+
+- Removed templating
+- Added support for Python 3.12
+- Dropped support for Python 3.8
+
 ## 0.4.2 (2023-06-09)
 
  - Replace pandas append function with concat
diff --git a/README.md b/README.md
index b79064b..b58a092 100644
--- a/README.md
+++ b/README.md
@@ -6,26 +6,20 @@
 [![Documentation Status](https://img.shields.io/badge/docs-passing-4a4c4c1.svg)](https://comic.github.io/evalutils/)
 [![Code style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
 
-evalutils helps challenge administrators easily create evaluation
-containers for grand-challenge.org.
+evalutils contains useful functions for evaluating machine learning models in the context of medical imaging.
 
   - Free software: MIT license
   - Documentation: <https://comic.github.io/evalutils/>.
 
 ## Features
 
-  - Generation your challenge evaluation project boilerplate using
-    [Cookiecutter](https://github.com/audreyr/cookiecutter)
-  - Scripts to build, test and export your generated docker container
-    for grand-challenge.org
-  - Loading of CSV, ITK and Pillow compatible prediction files
-  - Validation of submitted predictions
-  - Interface to SciKit-Learn metrics and Pandas aggregations
   - Bounding box annotations with Jaccard Index calculations
+  - Calculations of bootstrapped ROC curves
+  - Scoring for detection tasks
+  - Efficient calculation of confusion matrices, jaccard scores, dice scores, hausdorff distances,
+    absolute volume differences, and relative volume differences
 
 ## Getting Started
 
-[evalutils](https://github.com/comic/evalutils) requires Python 3.8 or
-above, and can be installed from `pip`.
-Please see the [Getting Started](https://comic.github.io/evalutils/usage.html)
-documentation for more details.
+[evalutils](https://github.com/comic/evalutils) requires Python 3.9 or above, and can be installed from `pip`.
+Please see the [Getting Started](https://comic.github.io/evalutils/usage.html) documentation for more details.
diff --git a/docs/conf.py b/docs/conf.py
index 3750d09..f6df797 100755
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -20,7 +20,6 @@
 import os
 import sys
 from importlib.metadata import version as _get_version
-from typing import Dict, List
 
 evalutils_version = _get_version("evalutils")
 
@@ -111,7 +110,7 @@ def setup(app):
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path: List[str] = []
+html_static_path: list[str] = []
 
 
 # -- Options for HTMLHelp output ---------------------------------------
@@ -122,7 +121,7 @@ def setup(app):
 
 # -- Options for LaTeX output ------------------------------------------
 
-latex_elements: Dict[str, str] = {
+latex_elements: dict[str, str] = {
     # The paper size ('letterpaper' or 'a4paper').
     #
     # 'papersize': 'letterpaper',
diff --git a/docs/contributing.rst b/docs/contributing.rst
deleted file mode 100644
index 40c5d1f..0000000
--- a/docs/contributing.rst
+++ /dev/null
@@ -1,114 +0,0 @@
-.. highlight:: shell
-
-============
-Contributing
-============
-
-Contributions are welcome, and they are greatly appreciated! Every little bit
-helps, and credit will always be given.
-
-You can contribute in many ways:
-
-Types of Contributions
-----------------------
-
-Report Bugs
-~~~~~~~~~~~
-
-Report bugs at https://github.com/comic/evalutils/issues.
-
-If you are reporting a bug, please include:
-
-* Your operating system name and version.
-* Any details about your local setup that might be helpful in troubleshooting.
-* Detailed steps to reproduce the bug.
-
-Fix Bugs
-~~~~~~~~
-
-Look through the GitHub issues for bugs. Anything tagged with "bug" and "help
-wanted" is open to whoever wants to implement it.
-
-Implement Features
-~~~~~~~~~~~~~~~~~~
-
-Look through the GitHub issues for features. Anything tagged with "enhancement"
-and "help wanted" is open to whoever wants to implement it.
-
-Write Documentation
-~~~~~~~~~~~~~~~~~~~
-
-evalutils could always use more documentation, whether as part of the
-official evalutils docs, in docstrings, or even on the web in blog posts,
-articles, and such.
-
-Submit Feedback
-~~~~~~~~~~~~~~~
-
-The best way to send feedback is to file an issue at https://github.com/comic/evalutils/issues.
-
-If you are proposing a feature:
-
-* Explain in detail how it would work.
-* Keep the scope as narrow as possible, to make it easier to implement.
-* Remember that this is a volunteer-driven project, and that contributions
-  are welcome :)
-
-Get Started!
-------------
-
-Ready to contribute? Here's how to set up `evalutils` for local development.
-
-1. Fork the `evalutils` repo on GitHub.
-2. Clone your fork locally::
-
-    $ git clone git@github.com:your_name_here/evalutils.git
-
-3. Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development::
-
-    $ mkvirtualenv evalutils
-    $ cd evalutils/
-    $ python setup.py develop
-
-4. Create a branch for local development::
-
-    $ git checkout -b name-of-your-bugfix-or-feature
-
-   Now you can make your changes locally.
-
-5. When you're done making changes, check that your changes pass flake8 and the
-   tests, including testing other Python versions with tox::
-
-    $ flake8 evalutils tests
-    $ python setup.py test or py.test
-    $ tox
-
-   To get flake8 and tox, just pip install them into your virtualenv.
-
-6. Commit your changes and push your branch to GitHub::
-
-    $ git add .
-    $ git commit -m "Your detailed description of your changes."
-    $ git push origin name-of-your-bugfix-or-feature
-
-7. Submit a pull request through the GitHub website.
-
-Pull Request Guidelines
------------------------
-
-Before you submit a pull request, check that it meets these guidelines:
-
-1. The pull request should include tests.
-2. If the pull request adds functionality, the docs should be updated. Put
-   your new functionality into a function with a docstring, and add the
-   feature to the list in README.md.
-3. The pull request should work for Python 3.8, 3.9, 3.10 and 3.11. Check
-   https://travis-ci.org/comic/evalutils/pull_requests
-   and make sure that the tests pass for all supported Python versions.
-
-Tips
-----
-
-To run a subset of tests::
-
-$ py.test tests.test_evalutils
diff --git a/docs/index.rst b/docs/index.rst
index b794351..d756f14 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -1,28 +1,14 @@
 Welcome to evalutils's documentation!
 ======================================
 
-The automated evaluation system on `Grand Challenge`_ will run an instance of
-your evaluation docker container image on each new submission.
-The users submission will be extracted on ``/input/``, and your container
-is expected to calculate all of the metrics for this submission, and write them
-to ``/output/metrics.json``.
-If the metrics cannot be calculated, the container should write to ``stderr``
-and the last line of this will be passed to the user so that they can debug
-their submission.
-
-`evalutils`_ helps you do this by providing a package that helps you create
-a project structure, load and validate the submissions, score the submission,
-write the json file, and package this in a docker container image so that
-you can then upload it to `Grand Challenge`_.
+`evalutils`_ contains useful functions for evaluating machine learning models in the context of medical imaging.
 
 .. toctree::
    :maxdepth: 2
    :caption: Contents:
 
    installation
-   usage
    modules
-   contributing
 
 Indices and tables
 ==================
diff --git a/docs/modules.rst b/docs/modules.rst
index c979e06..8c257a6 100644
--- a/docs/modules.rst
+++ b/docs/modules.rst
@@ -2,27 +2,6 @@
 Modules
 =======
 
-
-Evalutils
----------
-
-.. py:module:: evalutils
-
-.. automodule:: evalutils.evalutils
-   :members:
-
-IO
---
-
-.. automodule:: evalutils.io
-   :members:
-
-Validators
-----------
-
-.. automodule:: evalutils.validators
-   :members:
-
 Scorers
 -------
 
@@ -37,8 +16,8 @@ Annotations
 .. autoclass:: BoundingBox()
    :members:
 
-Statistics
-----------
+Stats
+-----
 
 .. automodule:: evalutils.stats
    :members:
diff --git a/docs/usage.rst b/docs/usage.rst
deleted file mode 100644
index ab8f792..0000000
--- a/docs/usage.rst
+++ /dev/null
@@ -1,633 +0,0 @@
-=====
-Usage
-=====
-
-Getting Started
----------------
-
-This guide shows you how to use ``evalutils`` to generate either an
-evaluation container or an algorithm container for `Grand Challenge`_.
-Select the appropriate project below to get started.
-
-Evaluation container
---------------------
-
-This guide will show you how to use ``evalutils`` to generate an evaluation
-container for `Grand Challenge`_. In this example we will call our project
-``myproject``, substitute your project name wherever you see this.
-
-Prerequisites
-^^^^^^^^^^^^^
-
-Before you start you will need to have:
-
-* A local `docker`_ installation
-* Your challenge test set ground truth data, this can be a CSV file or a set of images
-* An idea about what metrics you want to score the submissions on
-
-Generate The Project Structure
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Evalutils contains a project generator based on `CookieCutter`_ that you can
-use to generate the boilerplate for your evaluation.
-Once you have installed evalutils you can see the options with:
-
-.. code-block:: console
-
-    $ evalutils init evaluation --help
-
-Say that you want to create an evaluation for ``myproject``, you can initialize
-it with:
-
-.. code-block:: console
-
-    $ evalutils init evaluation myproject
-
-You will then be prompted to choose a challenge type:
-
-.. code-block:: console
-
-    $ What kind of challenge is this? [Classification|Segmentation|Detection]:
-
-so type in your challenge type and press <enter>.
-The different challenge types that you can select are:
-
-- **Classification**:
-    The submission and ground truth are csv files with the same number of rows.
-    For instance, this evaluation could be used for scoring classification of whole images into 1 or multiple classes.
-    The result of the evaluation is not reported on the case level to prevent leaking of the ground truth data.
-- **Segmentation**:
-    A special case of a classification task, the difference is that the submission and ground truth are image files (eg, ITK images or a collection of PNGs).
-    For instance, this evaluation could be used for scoring structure segmentation in 3D images.
-    There are the same number images in the ground truth dataset as there are in each submission.
-    By default, the results per case are also reported.
-- **Detection**:
-    The submission and ground truth are csv files, but with differing number of rows.
-    For instance, this evaluation could be used for scoring detection of tumours in images.
-    For this sort of challenge, you may have many candidate points and many ground truth points per case.
-    By default, the results per case are also reported.
-
-If you do not have a local python 3.8+ environment you can also
-generate your project with docker by running a container and sharing your current user id:
-
-.. code-block:: console
-
-    $ docker run -it --rm -u `id -u` -v $(pwd):/usr/src/myapp -w /usr/src/myapp python:3 bash -c "pip install evalutils && evalutils init evaluation myproject"
-
-Either of these commands will generate a folder called ``myproject``
-with everything you need to get started.
-
-It is a good idea to commit your project to git right now. You can do this with:
-
-.. code-block:: console
-
-    $ cd myproject
-    $ git init
-    $ git lfs install   (see the warning below)
-    $ git add --all
-    $ git commit -m "Initial Commit"
-
-.. warning:: The test set ground truth will be stored in this repo,
-    so remember to use a private repo if you're going to push this to github or gitlab,
-    and use `git lfs`_ if your ground truth data are large.
-
-    The .gitattributes file at the root of the repository specifies all the files which should be
-    tracked by git-lfs. By default all files in the ground truth and test directories
-    are configured to be tracked by git-lfs, but they will only be registered
-    once the `git lfs`_ extension is installed on your system and the ``git lfs install``
-    command has been issued inside the generated repository.
-
-
-The structure of the project will be:
-
-.. code-block:: console
-
-    .
-    └── myproject
-        ├── build.sh            # Builds your evaluation container
-        ├── Dockerfile          # Defines how to build your evaluation container
-        ├── evaluation.py       # Contains your evaluation code - this is where you will extend the Evaluation class
-        ├── export.sh           # Exports your container to a .tar file for use on grand-challenge.org
-        ├── .gitattributes      # Define which files git should put under git-lfs
-        ├── .gitignore          # Define which files git should ignore
-        ├── ground-truth        # A folder that contains your ground truth annotations
-        │   └── reference.csv   # In this example the ground truth is a csv file
-        ├── README.md           # For describing your evaluation to others
-        ├── requirements.in     # The python dependencies of your evaluation container - add any new dependencies here
-        ├── requirements.txt    # The compiled python dependencies, update this with `pip-compile`
-        ├── test                # A folder that contains an example submission for testing
-        │   └── submission.csv  # In this example the participants will submit a csv file
-        └── test.sh             # A script that runs your evaluation container on the test submission
-
-For Segmentation tasks, some example mhd/zraw files will be in the ground-truth and test directories instead.
-
-The most important file is ``evaluation.py``.
-This is the file where you will extend the ``Evaluation`` class and implement the evaluation for your challenge.
-In this file, a new class has been created for you, and it is instantiated and run with:
-
-.. code-block:: python
-
-    if __name__ == "__main__":
-        Myproject().evaluate()
-
-
-This is all that is needed for ``evalutils`` to perform the evaluation and generate the output for each new submission.
-The superclass of ``Evaluation`` is what you need to adapt to your specific challenge.
-
-.. warning:: When designing your submission format do NOT use subfolders to differentiate between tasks.
-    If you have multiple tasks, you should be using multiple phases and evaluations instead.
-    On grand challenge all common path prefixes will be removed from the submitted files,
-    so your evaluation should only work with flat lists of files.
-
-Classification Tasks
-~~~~~~~~~~~~~~~~~~~~
-
-The boilerplate for classification challenges looks like this:
-
-.. code-block:: python
-
-    class Myproject(ClassificationEvaluation):
-        def __init__(self):
-            super().__init__(
-                file_loader=CSVLoader(),
-                validators=(
-                    ExpectedColumnNamesValidator(expected=("case", "class",)),
-                    NumberOfCasesValidator(num_cases=8),
-                ),
-                join_key="case",
-            )
-
-        def score_aggregates(self):
-            return {
-                "accuracy_score": accuracy_score(
-                    self._cases["class_ground_truth"],
-                    self._cases["class_prediction"],
-                 ),
-            }
-
-In this case the evaluation is loading csv files, so uses an instance ``CSVLoader`` which will do the loading of the data.
-In this example, both the ground truth and the prediction CSV files will contain the columns `case` (an index) and `class` (the predicted class of this case).
-We want to validate that the correct columns appear in both the ground truth and submitted predictions, so we use the ``ExpectedColumnNamesValidator`` with the names of the columns we expect to find.
-We also use the ``NumberOfCasesValidator`` to check that the correct number of cases has been submitted by the challenge participant.
-See :mod:`evalutils.validators` for a list of other validators that you can use.
-
-The ground truth and predictions will be loaded into two DataFrames.
-The last argument is a ``join_key``, the is the name of the column that will appear in both DataFrames that serves as an index to join the dataframes on in order to create ``self._cases``.
-The ``join_key`` is manditory when you use a ``CSVLoader``.
-This should be set to some sort of common index, such as a `case` identifier.
-When loading in files they are first going to be sorted so you might not need a ``join_key``, but you could also write a function that matches the cases based on filename.
-
-.. warning:: It is best practice to include an integer in the (file) name that uniquely defines each case.
-    For instance, name your testing set files case_001, case_002, ... etc.
-
-The last part is performing the actual evaluation.
-In this example we are only getting one number per submission, the accuracy score.
-This number is calculated using ``sklearn.metrics.accuracy_score``.
-The ``self._cases`` data frame will contain all of the columns that you expect, and for those that have not been joined they will be available as ``"<column_name>_ground_truth"`` and ``"<column_name>_prediction"``.
-
-If you need to score cases individually before aggregating them, you should remove the implementation of ``score_aggregates`` and implement ``score_case`` instead.
-
-Segmentation Tasks
-~~~~~~~~~~~~~~~~~~
-
-For segmentation tasks, the generated code will look like this:
-
-.. code-block:: python
-
-    class Myproject(ClassificationEvaluation):
-        def __init__(self):
-            super().__init__(
-                file_loader=SimpleITKLoader(),
-                validators=(
-                    NumberOfCasesValidator(num_cases=2),
-                    UniquePathIndicesValidator(),
-                    UniqueImagesValidator(),
-                ),
-            )
-
-        def score_case(self, *, idx, case):
-            gt_path = case["path_ground_truth"]
-            pred_path = case["path_prediction"]
-
-            # Load the images for this case
-            gt = self._file_loader.load_image(gt_path)
-            pred = self._file_loader.load_image(pred_path)
-
-            # Check that they're the right images
-            assert self._file_loader.hash_image(gt) == case["hash_ground_truth"]
-            assert self._file_loader.hash_image(pred) == case["hash_prediction"]
-
-            # Cast to the same type
-            caster = SimpleITK.CastImageFilter()
-            caster.SetOutputPixelType(SimpleITK.sitkUInt8)
-            gt = caster.Execute(gt)
-            pred = caster.Execute(pred)
-
-            # Score the case
-            overlap_measures = SimpleITK.LabelOverlapMeasuresImageFilter()
-            overlap_measures.Execute(gt, pred)
-
-            return {
-                'FalseNegativeError': overlap_measures.GetFalseNegativeError(),
-                'FalsePositiveError': overlap_measures.GetFalsePositiveError(),
-                'MeanOverlap': overlap_measures.GetMeanOverlap(),
-                'UnionOverlap': overlap_measures.GetUnionOverlap(),
-                'VolumeSimilarity': overlap_measures.GetVolumeSimilarity(),
-                'JaccardCoefficient': overlap_measures.GetJaccardCoefficient(),
-                'DiceCoefficient': overlap_measures.GetDiceCoefficient(),
-                'pred_fname': pred_path.name,
-                'gt_fname': gt_path.name,
-            }
-
-Here, we are loading ITK files in the ground-truth and test folders using ``SimpleITKLoader``.
-See :mod:`evalutils.io` for the other image loaders you could use.
-By default, the files will be matched together based on the first integer found in the filename, so name your ground truth files, for example, case_001.mha, case_002.mha, etc.
-Have the participants for your challenge do the same.
-
-The loader will try to load all of the files in the ground-truth and submission folders.
-To check that the correct number of images were submitted by the participant and loaded we use ``NumberOfCasesValidator``, and check that the images are unique by using ``UniquePathIndicesValidator`` and ``UniqueImagesValidator``
-
-The ``score_case`` function will calculate the score for each case, in this case we're calculating some overlap measures using ``SimpleITK``.
-The images are not stored in the case dataframe to save memory, so first they are loaded using the file loader, and are then checked that they are the valid images by calculating the hash.
-The filenames are also stored for the case for matching later on grand-challenge.
-
-The aggregate results are automatically calculated using ``score_aggregates``, which calls ``DataFrame.describe()``.
-By default, this will calculate the mean, quartile ranges and counts of each individual metric.
-
-Detection Tasks
-~~~~~~~~~~~~~~~
-
-The generated boilerplate for detection tasks is:
-
-.. code-block:: python
-
-    class Myproject(DetectionEvaluation):
-        def __init__(self):
-            super().__init__(
-                file_loader=CSVLoader(),
-                validators=(
-                    ExpectedColumnNamesValidator(
-                        expected=("image_id", "x", "y", "score")
-                    ),
-                ),
-                join_key="image_id",
-                detection_radius=1.0,
-                detection_threshold=0.5,
-            )
-
-        def get_points(self, *, case, key):
-            """
-            Converts the set of ground truth or predictions for this case, into
-            points that represent true positives or predictions
-            """
-            try:
-                points = case.loc[key]
-            except KeyError:
-                # There are no ground truth/prediction points for this case
-                return []
-
-            return [
-                (p["x"], p["y"])
-                for _, p in points.iterrows()
-                if p["score"] > self._detection_threshold
-            ]
-
-In this case, we are loading a CSV file with ``CSVLoader``, but do not validate the number of rows as they can be different between the ground truth and submissions.
-We validate the column headers in both files.
-In this case, we identify the cases with ``image_id``, and both files contain ``x`` and ``y`` locations, with a confidence score of ``score``.
-In the ground truth dataset the score should be set to 1.
-
-By default, The predictions will be thresholded at ``detection_threshold``.
-The detection evaluation will count the closest prediction that lies within distance ``detection_radius`` from the ground truth point as a true positive.
-See :mod:`evalutils.scorers` for more information on the algorithm.
-
-The only function that needs to be implemented is ``get_points``, which converts a case row to a list of points which are later matched.
-In this case, we're acting on 2D images, but you could extend ``(p["x"], p["y"])`` to say ``(p["x"], p["y"], p["z"])`` if you have 3D data.
-
-By default, the f1 score, precision and accuracy are calculated for each case, see the ``DetectionEvaluation`` class for more information.
-
-Add The Ground Truth and Test Data
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-The next step is to add your ground truth and test data (an example submission) to the repo.
-If using CSV data simply update the ``ground-truth/reference.csv`` file, and then update the expected column names and join key in evaluate.py.
-Otherwise, see :mod:`evalutils.io` for other loaders such as the ones for ITK files or images.
-You can also add your own loader by extending the ``FileLoader`` class.
-
-Adapt The Evaluation
-^^^^^^^^^^^^^^^^^^^^
-
-Change the function in the boilerplate to fit your needs, refer to the superclass methods for more information on return types.
-See :class:`evalutils.Evaluation` for more possibilities.
-
-Build, Test and Export
-^^^^^^^^^^^^^^^^^^^^^^
-
-When you're ready to test your evaluation you can simply invoke
-
-.. code-block:: console
-
-    $ ./test.sh
-
-This will build your docker container, add the test data as a temporary volume, run the evaluation, and then ``cat /output/metrics.json``.
-If the output looks ok, then you're ready to go.
-
-You can export the evaluation container with
-
-.. code-block:: console
-
-    $ ./export.sh
-
-which will create myproject.tar in the folder.
-You can then upload this directly to `Grand Challenge`_ on your evaluation methods page.
-
-
-Algorithm container
--------------------
-
-This guide will show you how to use ``evalutils`` to generate an algorithm
-container for `Grand Challenge`_. In this example we will call our project
-``myproject``, substitute your project name wherever you see this.
-
-Prerequisites
-^^^^^^^^^^^^^
-
-Before you start you will need to have:
-
-* A local `docker`_ installation
-* Some test images for your algorithm, preferably with expected output
-
-Generate The Project Structure
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Evalutils contains a project generator based on `CookieCutter`_ that you can
-use to generate the boilerplate for your algorithm.
-Once you have installed evalutils you can see the options with:
-
-.. code-block:: console
-
-    $ evalutils init algorithm --help
-
-Say that you want to create an evaluation for ``myproject``, you can initialize
-it with:
-
-.. code-block:: console
-
-    $ evalutils init algorithm myproject
-
-You will then be prompted to choose an algorithm type:
-
-.. code-block:: console
-
-    $ What kind of algorithm is this? [Classification|Segmentation|Detection]:
-
-so type in your algorithm type and press <enter>.
-The different algorithm types that you can select are:
-
-- **Classification**:
-    This type of algorithm takes in an image (eg, ITK images or a collection of PNGs) and outputs a `/output/results.json` file.
-    For instance, this algorithm could be used for classification of whole images into 1 or multiple classes.
-    By default, the algorithm outputs a single `/output/results.json` which lists the results per case.
-- **Segmentation**:
-    A special case of a classification task, that takes in an image and outputs an image file to `/output/images/` (eg, ITK images or a collection of PNGs).
-    For instance, this algorithm could be used for structure segmentation in 3D images.
-    By default, the algorithm outputs an image file at `/output/images/` per case and a single `/output/results.json` file with additional information for all cases.
-- **Detection**:
-    This type of algorithm detects one or more candidates in an image and returns the positions relative to the image.
-    For instance, this evaluation could be used for detection of tumours in images.
-    By default, the algorithm outputs a single `/output/results.json` which lists the results per case.
-
-If you do not have a local python 3.8+ environment you can also
-generate your project with docker by running a container and sharing your current user id:
-
-.. code-block:: console
-
-    $ docker run -it --rm -u `id -u` -v $(pwd):/usr/src/myapp -w /usr/src/myapp python:3 bash -c "pip install evalutils && evalutils init algorithm myproject"
-
-Either of these commands will generate a folder called ``myproject``
-with everything you need to get started.
-
-It is a good idea to commit your project to git right now. You can do this with:
-
-.. code-block:: console
-
-    $ cd myproject
-    $ git init
-    $ git lfs install   (see the warning below)
-    $ git add --all
-    $ git commit -m "Initial Commit"
-
-.. warning:: The test input images and the expected output will be stored in this repo,
-    so remember to use a private repo if you're going to push this to github or gitlab,
-    and use `git lfs`_ if your ground truth data are large.
-
-    The .gitattributes file at the root of the repository specifies all the files which should be
-    tracked by git-lfs. By default all files in the test directories
-    are configured to be tracked by git-lfs, but they will only be registered
-    once the `git lfs`_ extension is installed on your system and the ``git lfs install``
-    command has been issued inside the generated repository.
-
-
-The structure of the project will be:
-
-.. code-block:: console
-
-    .
-    └── myproject
-        ├── build.sh                 # Builds your algorithm container
-        ├── Dockerfile               # Defines how to build your algorithm container
-        ├── export.sh                # Exports your algorithm container to a .tar file for use on grand-challenge.org
-        ├── .gitattributes           # Define which files git should put under git-lfs
-        ├── .github/workflows/ci.yml # Contains a CI configuration file for github workflows for your project
-        ├── .gitignore               # Define which files git should ignore
-        ├── process.py               # Contains your algorithm code - this is where you will extend the BaseAlgorithm class
-        ├── README.md                # For describing your algorithm to others
-        ├── requirements.in          # The python dependencies of your algorithm container - add any new dependencies here
-        ├── requirements.txt         # The compiled python dependencies, update this with `pip-compile`
-        ├── test                     # A folder that contains an example test image for testing
-        │   ├── 1.0.000.000000*.mhd  # An example test image metaio header file
-        │   ├── 1.0.000.000000*.zraw # An example test image metaio data file
-        │   └── expected_output.json # Output file expected to be produced by the algorithm container
-        └── test.sh                  # A script that runs your algorithm container using the example test image and validates the output
-
-The most important file is ``process.py``.
-This is the file where you will extend the ``Algorithm`` class and implement your algorithm.
-In this file, a new class has been created for you, and it is instantiated and run with:
-
-.. code-block:: python
-
-    if __name__ == "__main__":
-        Myproject().process()
-
-
-This is all that is needed for ``evalutils`` to run the algorithm and process input images.
-The subclass of ``Algorithm`` is what you need to modify for your specific algorithms.
-
-By default all algorithms will try to load all files in the /input directory using ``SimpleITKLoader`` for loading the data.
-After successfully loading a single image a ``SimpleITK.Image`` object is ready to be manipulated by the algorithms in
-the ``predict`` method for algorithm tasks like classification, segmentation, or detection.
-
-
-Classification Algorithm
-~~~~~~~~~~~~~~~~~~~~~~~~
-
-The boilerplate for classification algorithms looks like this:
-
-.. code-block:: python
-
-    class Myproject(ClassificationAlgorithm):
-        def __init__(self):
-            super().__init__(
-                validators=dict(
-                    input_image=(
-                        UniqueImagesValidator(),
-                        UniquePathIndicesValidator(),
-                    )
-                ),
-
-        def predict(self, *, input_image: SimpleITK.Image) -> Dict:
-            # Checks if there are any nodules voxels (> 1) in the input image
-            return {
-                "values_exceeding_one": bool(np.any(SimpleITK.GetArrayFromImage(input_image) > 1))
-            }
-
-
-The input images can be validated by specifying validators. Here, we want to validate that the input images are unique,
-so we use the ``UniqueImagesValidator`` and ``UniquePathIndicesValidator``.
-See :mod:`evalutils.validators` for a list of other validators that you can use.
-
-All that needs to be modified is the ``predict`` method of your Algorithm class. By default, it takes an input ``SimpleITK.Image``
-and expects a dictionary that contains some values based on the image.
-In this example it takes the input image, converts it to a ``numpy.ndarray``, checks if there is any value larger than 1 in the image, and writes the boolean result to a dictionary.
-
-The output dictionary can have an arbitrary number of key/value pairs. Extending the outputs can be done by adding new dictionary keys and associated values.
-
-The resulting dictionary will be written to ``/output/results.json`` output file by the super class after running the ``predict`` method on all inputs.
-
-Segmentation Algorithm
-~~~~~~~~~~~~~~~~~~~~~~
-
-For segmentation tasks, the generated code will look like this:
-
-.. code-block:: python
-
-    class Myproject(SegmentationAlgorithm):
-        def __init__(self):
-            super().__init__(
-                validators=dict(
-                    input_image=(
-                        UniqueImagesValidator(),
-                        UniquePathIndicesValidator(),
-                    )
-                ),
-
-        def predict(self, *, input_image: SimpleITK.Image) -> SimpleITK.Image:
-            # Segment all values greater than 2 in the input image
-            return SimpleITK.BinaryThreshold(
-                image1=input_image, lowerThreshold=2, insideValue=1, outsideValue=0
-            )
-
-
-Similar as before, all that needs to be modified is the ``predict`` method of your Algorithm class. By default, it takes an input ``SimpleITK.Image``
-and expects another ``SimpleITK.Image`` as an output.
-
-In this example it takes the input image, thresholds this for all values greater than or equal to 2 and returns the resulting image.
-
-Besides the default ``/output/results.json`` output file the SegmentationAlgorithm outputs the resulting images at: ``/output/images/``.
-
-
-Detection Algorithm
-~~~~~~~~~~~~~~~~~~~
-
-The generated boilerplate for detection tasks is:
-
-.. code-block:: python
-
-    class Myproject(DetectionAlgorithm):
-        def __init__(self):
-            super().__init__(
-                validators=dict(
-                    input_image=(
-                        UniqueImagesValidator(),
-                        UniquePathIndicesValidator(),
-                    )
-                ),
-
-        def predict(self, *, input_image: SimpleITK.Image) -> DataFrame:
-            # Extract a numpy array with image data from the SimpleITK Image
-            image_data = SimpleITK.GetArrayFromImage(input_image)
-
-            # Detection: Compute connected components of the maximum values
-            # in the input image and compute their center of mass
-            sample_mask = image_data >= np.max(image_data)
-            labels, num_labels = label(sample_mask)
-            candidates = center_of_mass(
-                input=sample_mask, labels=labels, index=np.arange(num_labels) + 1
-            )
-
-            # Scoring: Score each candidate cluster with the value at its center
-            candidate_scores = [
-                image_data[tuple(coord)]
-                for coord in np.array(candidates).astype(np.uint16)
-            ]
-
-            # Serialize candidates and scores as a list of dictionary entries
-            data = self._serialize_candidates(
-                candidates=candidates,
-                candidate_scores=candidate_scores,
-                ref_image=input_image,
-            )
-
-            # Convert serialized candidates to a pandas.DataFrame
-            return DataFrame(data)
-
-
-The only function that needs to be implemented is ``predict``, which should extract a list of candidate points from the input image and
-return the candidates with some associated scores or labels to a ``pandas.DataFrame``.
-
-The ``_serialize_candidates`` helper function takes in a list of candidates in image coordinate space and converts these to world coordinates given a reference ``SimpleItk.Image``.
-Additionally, the function adds scores per candidate.
-
-The resulting DataFrame is added to the ``/output/results.json``.
-
-Add The Test Data And The Expected Output File
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-The next step is to add your test data (an example image and a json file with the expected output) to the repo.
-The test images go into the ``test`` folder in your repo.
-To update the expected output simply update the ``test/expected_output.json`` file.
-
-Adapt The Algorithm
-^^^^^^^^^^^^^^^^^^^
-
-Change the function in the boilerplate to fit your needs, refer to the superclass methods for more information on return types.
-See :class:`evalutils.BaseAlgorithm` for more possibilities.
-
-Build, Test and Export
-^^^^^^^^^^^^^^^^^^^^^^
-
-When you're ready to test your algorithm you can simply invoke
-
-.. code-block:: console
-
-    $ ./test.sh
-
-This will build your docker container, add the test data as a temporary volume, run the algorithm, ``cat /output/results.json``,
-and test if the ``/output/results.json`` matches ``expected_output.json`` in the test folder of ``myproject``.
-If the output looks ok and prints ``Tests successfully passed...``, then you're ready to go.
-
-You can export the algorithm container with
-
-.. code-block:: console
-
-    $ ./export.sh
-
-which will create ``myproject.tar.gz`` in the folder.
-You can then upload this directly to `Grand Challenge`_ on your algorithms page.
-
-
-
-.. _`Grand Challenge`: https://grand-challenge.org
-.. _docker: https://www.docker.com/
-.. _`git lfs`: https://git-lfs.github.com/
-.. _`CookieCutter`: https://github.com/audreyr/cookiecutter
diff --git a/evalutils/__init__.py b/evalutils/__init__.py
index 6af074d..e69de29 100644
--- a/evalutils/__init__.py
+++ b/evalutils/__init__.py
@@ -1,17 +0,0 @@
-from .evalutils import (
-    ClassificationAlgorithm,
-    ClassificationEvaluation,
-    DetectionAlgorithm,
-    DetectionEvaluation,
-    Evaluation,
-    SegmentationAlgorithm,
-)
-
-__all__ = [
-    "ClassificationAlgorithm",
-    "ClassificationEvaluation",
-    "DetectionAlgorithm",
-    "DetectionEvaluation",
-    "Evaluation",
-    "SegmentationAlgorithm",
-]
diff --git a/evalutils/__main__.py b/evalutils/__main__.py
index decf7df..14aa6a6 100644
--- a/evalutils/__main__.py
+++ b/evalutils/__main__.py
@@ -1,4 +1,18 @@
-from .cli import main
+import warnings
+
+
+def main():
+    warnings.simplefilter("always", DeprecationWarning)
+
+    warnings.warn(
+        "The CLI and templating using evalutils is deprecated. "
+        "Use the templates provided by the challenge organisers instead.",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+
+    raise (SystemExit(1))
+
 
 if __name__ == "__main__":
-    main(prog_name="evalutils")
+    main()
diff --git a/evalutils/cli.py b/evalutils/cli.py
deleted file mode 100644
index 51fe013..0000000
--- a/evalutils/cli.py
+++ /dev/null
@@ -1,204 +0,0 @@
-import re
-import sys
-from importlib.metadata import version
-from pathlib import Path
-from typing import List
-
-import click
-from cookiecutter.exceptions import FailedHookException
-from cookiecutter.main import cookiecutter
-
-evalutils_version = version("evalutils")
-
-EVALUATION_CHOICES = ["Classification", "Segmentation", "Detection"]
-ALGORITHM_CHOICES = EVALUATION_CHOICES
-FORBIDDEN_NAMES = ["evalutils", "pandas", "Evaluation", "Algorithm"]
-MODULE_REGEX = r"^[_a-zA-Z][_a-zA-Z0-9]+$"
-
-
-@click.group(context_settings=dict(help_option_names=["-h", "--help"]))
-@click.version_option(evalutils_version, "-v", "--version")
-def main():
-    pass
-
-
-@main.group(name="init", short_help="Initialise a project.")
-def init():
-    pass
-
-
-def validate_python_module_name_fn(option):
-    def validate_python_module_name_string(ctx, param, arg):
-        if len(arg.strip()) == 0:
-            raise click.BadParameter(f"{option.upper()} should be non empty")
-
-        if not re.match(MODULE_REGEX, arg) or arg in FORBIDDEN_NAMES:
-            raise click.BadParameter(
-                f"{arg!r} is not a valid Python module name"
-            )
-
-        return arg
-
-    return validate_python_module_name_string
-
-
-class AbbreviatedChoice(click.Choice):
-    def __init__(self, choices: List[str]):
-        super().__init__(choices=choices, case_sensitive=True)
-        self._abbreviations = [e[0].upper() for e in choices]
-        self._choices_upper = list(map(str.upper, choices))
-        if len(set(self._abbreviations)) != len(choices):
-            raise ValueError(
-                "First letters of choices for AbbreviatedChoices should be unique!"
-            )
-
-    def get_metavar(self, param):
-        return "[{}]".format(
-            "|".join([f"({e[0]}){e[1:]}" for e in self.choices])
-        )
-
-    def convert(self, value, param, ctx):
-        value = value.upper()
-        if value in self._abbreviations:
-            value = self.choices[self._abbreviations.index(value)]
-        elif value in self._choices_upper:
-            value = self.choices[self._choices_upper.index(value)]
-        return super().convert(value=value, param=param, ctx=ctx)
-
-
-@init.command(
-    name="evaluation", short_help="Initialise an evaluation project."
-)
-@click.argument(
-    "challenge_name", callback=validate_python_module_name_fn("challenge_name")
-)
-@click.option(
-    "--kind",
-    type=AbbreviatedChoice(EVALUATION_CHOICES),
-    prompt="What kind of challenge is this?",
-)
-@click.option("--dev", is_flag=True)
-def init_evaluation(challenge_name, kind, dev):
-    template_dir = Path(__file__).parent / "templates" / "container"
-    try:
-        cookiecutter(
-            template=str(template_dir.absolute()),
-            no_input=True,
-            extra_context={
-                "full_project_name": challenge_name,
-                "task_kind": kind,
-                "template_kind": "Evaluation",
-                **_get_cookiecutter_base_context(dev_build=dev),
-            },
-        )
-        click.echo(f"Created project {challenge_name}")
-    except FailedHookException:
-        exit(1)
-
-
-def validate_size_format_fn(can_be_empty=False):
-    def validate_size_format(ctx, param, arg):
-        pattern = r"(^\d+[kgtpe]$)|(^$)" if can_be_empty else r"^\d+[kgtpe]$"
-        fmt = re.compile(pattern, flags=re.IGNORECASE)
-        while not re.match(fmt, arg.strip()):
-            arg = click.prompt(
-                f"Enter a valid size format ({fmt.pattern}):", default="1G"
-            )
-        return arg
-
-    return validate_size_format
-
-
-def req_cpu_capabilities_prompt(ctx, param, reqs):
-    if not reqs:
-        while True:
-            capability = "something"
-            reqs = ()
-            while capability != "":
-                capability = click.prompt(
-                    f"Required node capability? (e.g.: avx) *{reqs}*",
-                    type=click.STRING,
-                    default="",
-                )
-                if capability != "":
-                    reqs += (capability,)
-            if click.confirm(
-                f"Are *{reqs}* all required node capabilities?", True
-            ):
-                break
-    return reqs
-
-
-def req_gpu_prompt(ctx, param, req_gpu_count):
-    gpu_memory = ctx.params.get("req_gpu_memory")
-    gpu_compute_capability = ctx.params.get("req_gpu_compute_capability")
-    if req_gpu_count > 0:
-        if not gpu_compute_capability:
-            gpu_compute_capability = click.prompt(
-                "Required gpu compute capability (version string e.g.: 1.5.0)",
-                default="",
-                type=click.STRING,
-            )
-        if not gpu_memory:
-            gpu_memory = validate_size_format_fn(can_be_empty=True)(
-                None,
-                None,
-                click.prompt(
-                    "Required gpu memory? (e.g.: 4G)",
-                    default="",
-                    type=click.STRING,
-                ),
-            )
-    else:
-        gpu_memory = ""
-        gpu_compute_capability = ""
-    ctx.params["req_gpu_compute_capability"] = gpu_compute_capability
-    ctx.params["req_gpu_memory"] = gpu_memory
-    return req_gpu_count
-
-
-@init.command(name="algorithm", short_help="Initialise an algorithm project.")
-@click.argument(
-    "algorithm_name", callback=validate_python_module_name_fn("algorithm_name")
-)
-@click.option(
-    "--kind",
-    type=AbbreviatedChoice(ALGORITHM_CHOICES),
-    prompt="What kind of algorithm is this?",
-)
-@click.option("--dev", is_flag=True)
-def init_algorithm(algorithm_name, kind, dev):
-    template_dir = Path(__file__).parent / "templates" / "container"
-    try:
-        cookiecutter(
-            template=str(template_dir.absolute()),
-            no_input=True,
-            extra_context={
-                "full_project_name": algorithm_name,
-                "task_kind": kind,
-                "template_kind": "Algorithm",
-                **_get_cookiecutter_base_context(dev_build=dev),
-            },
-        )
-        click.echo(f"Created project {algorithm_name}")
-    except FailedHookException:
-        exit(1)
-
-
-def _get_cookiecutter_base_context(*, dev_build):
-    dev_build = 1 if dev_build else 0
-
-    if dev_build:
-        requirements = {
-            f"file:vendor/evalutils-{evalutils_version}-py3-none-any.whl#egg=evalutils": ""  # noqa: B950
-        }
-    else:
-        requirements = {"evalutils": f"=={evalutils_version}"}
-
-    return {
-        "dev_build": dev_build,
-        "evalutils_version": evalutils_version,
-        "python_major_version": sys.version_info.major,
-        "python_minor_version": sys.version_info.minor,
-        "requirements": requirements,
-    }
diff --git a/evalutils/evalutils.py b/evalutils/evalutils.py
deleted file mode 100644
index 9a73bc7..0000000
--- a/evalutils/evalutils.py
+++ /dev/null
@@ -1,711 +0,0 @@
-import json
-import logging
-from abc import ABC, abstractmethod
-from os import PathLike
-from pathlib import Path
-from typing import (
-    Any,
-    Callable,
-    Dict,
-    Iterable,
-    List,
-    Optional,
-    Pattern,
-    Set,
-    Tuple,
-    Union,
-)
-from warnings import warn
-
-import SimpleITK
-from pandas import DataFrame, Series, concat, merge
-
-from .exceptions import ConfigurationError, FileLoaderError, ValidationError
-from .io import (
-    CSVLoader,
-    FileLoader,
-    ImageLoader,
-    SimpleITKLoader,
-    first_int_in_filename_key,
-)
-from .scorers import score_detection
-from .validators import DataFrameValidator
-
-logger = logging.getLogger(__name__)
-
-DEFAULT_INPUT_PATH = Path("/input/")
-DEFAULT_ALGORITHM_OUTPUT_IMAGES_PATH = Path("/output/images/")
-DEFAULT_ALGORITHM_OUTPUT_FILE_PATH = Path("/output/results.json")
-DEFAULT_GROUND_TRUTH_PATH = Path("/opt/app/ground-truth/")
-DEFAULT_EVALUATION_OUTPUT_FILE_PATH = Path("/output/metrics.json")
-
-
-class Algorithm(ABC):
-    def __init__(
-        self,
-        *,
-        index_key: str = "input_image",
-        file_loaders: Optional[Dict[str, FileLoader]] = None,
-        file_filters: Optional[Dict[str, Optional[Pattern[str]]]] = None,
-        input_path: Path = DEFAULT_INPUT_PATH,
-        output_path: Path = DEFAULT_ALGORITHM_OUTPUT_IMAGES_PATH,
-        file_sorter_key: Optional[Callable] = None,
-        validators: Optional[Dict[str, Tuple[DataFrameValidator, ...]]] = None,
-        output_file: PathLike = DEFAULT_ALGORITHM_OUTPUT_FILE_PATH,
-    ):
-        """
-        The base class for all algorithms. Sets the environment and controls
-        the flow of the processing once `process` is called.
-
-
-        Parameters
-        ----------
-        index_key
-            Fileloader key which must be used for the index.
-            Default: `input_image`
-        file_loaders
-            The loaders that will be used to get all files.
-            Default: `evalutils.io.SimpleITKLoader` for `input_image`
-        file_filters
-            Regular expressions for filtering certain FileLoaders.
-            Default: no filtering.
-        input_path
-            The path in the container where the ground truth will be loaded
-            from. Default: `/input`
-        output_path
-            The path in the container where the output images will be written.
-            Default: `/output/images`
-        file_sorter_key
-            A function that determines how files in the input_path are sorted.
-            Default: `None` (alphanumerical)
-        validators
-            A dictionary containing the validators that will be used on the
-            loaded data per file_loader key. Default: {}
-        output_file
-            The path to the location where the results will be written.
-            Default: `/output/results.json`
-        """
-        self._index_key = index_key
-        self._input_path = input_path
-        self._output_path = output_path
-        self._file_sorter_key = file_sorter_key
-        self._output_file = output_file
-
-        self._ground_truth_cases = DataFrame()
-        self._predictions_cases = DataFrame()
-
-        self._cases: Dict[str, DataFrame] = {}
-
-        self._case_results: List[Dict] = []
-
-        self._validators: Dict[str, Tuple[DataFrameValidator, ...]] = (
-            {} if validators is None else validators
-        )
-        self._file_loaders: Dict[str, FileLoader] = (
-            dict(input_image=SimpleITKLoader())
-            if file_loaders is None
-            else file_loaders
-        )
-        self._file_filters: Dict[str, Optional[Pattern[str]]] = (
-            dict(input_image=None) if file_filters is None else file_filters
-        )
-
-        super().__init__()
-
-    def load(self):
-        for key, file_loader in self._file_loaders.items():
-            fltr = (
-                self._file_filters[key] if key in self._file_filters else None
-            )
-            self._cases[key] = self._load_cases(
-                folder=self._input_path,
-                file_loader=file_loader,
-                file_filter=fltr,
-            )
-
-    def _load_cases(
-        self,
-        *,
-        folder: Path,
-        file_loader: ImageLoader,
-        file_filter: Optional[Pattern[str]] = None,
-    ) -> DataFrame:
-        cases = None
-
-        for f in sorted(folder.glob("**/*"), key=self._file_sorter_key):
-            if file_filter is None or file_filter.match(str(f)):
-                try:
-                    new_cases = file_loader.load(fname=f)
-                except FileLoaderError:
-                    logger.warning(
-                        f"Could not load {f.name} using {file_loader}."
-                    )
-                else:
-                    if cases is None:
-                        cases = new_cases
-                    else:
-                        cases += new_cases
-            else:
-                logger.info(
-                    f"Skip loading {f.name} because it doesn't match {file_filter}."
-                )
-
-        if cases is None:
-            raise FileLoaderError(
-                f"Could not load any files in {folder} with " f"{file_loader}."
-            )
-
-        return DataFrame(cases)
-
-    def validate(self):
-        """Validates each dataframe for each fileloader separately"""
-        file_loaders_keys = [k for k in self._file_loaders.keys()]
-        for key in self._validators.keys():
-            if key not in file_loaders_keys:
-                raise ValueError(
-                    f"There is no file_loader associated with: {key}.\n"
-                    f"Valid file loaders are: {file_loaders_keys}"
-                )
-        for key, cases in self._cases.items():
-            if key in self._validators:
-                self._validate_data_frame(df=cases, file_loader_key=key)
-
-    def _validate_data_frame(self, *, df: DataFrame, file_loader_key: str):
-        for validator in self._validators[file_loader_key]:
-            validator.validate(df=df)
-
-    def process(self):
-        self.load()
-        self.validate()
-        self.process_cases()
-        self.save()
-
-    def process_cases(self, file_loader_key: Optional[str] = None):
-        if file_loader_key is None:
-            file_loader_key = self._index_key
-        self._case_results = []
-        for idx, case in self._cases[file_loader_key].iterrows():
-            self._case_results.append(self.process_case(idx=idx, case=case))
-
-    @abstractmethod
-    def process_case(self, *, idx: int, case: DataFrame) -> Dict:
-        raise NotImplementedError()
-
-    def save(self):
-        with open(str(self._output_file), "w") as f:
-            json.dump(self._case_results, f)
-
-    def _load_input_image(self, *, case) -> Tuple[SimpleITK.Image, Path]:
-        input_image_file_path = case["path"]
-
-        input_image_file_loader = self._file_loaders["input_image"]
-        if not isinstance(input_image_file_loader, ImageLoader):
-            raise RuntimeError(
-                "The used FileLoader was not of subclass ImageLoader"
-            )
-
-        # Load the image for this case
-        input_image = input_image_file_loader.load_image(input_image_file_path)
-
-        # Check that it is the expected image
-        if input_image_file_loader.hash_image(input_image) != case["hash"]:
-            raise RuntimeError("Image hashes do not match")
-
-        return input_image, input_image_file_path
-
-    @abstractmethod
-    def predict(self, *, input_image: SimpleITK.Image) -> Any:
-        raise NotImplementedError()
-
-
-class DetectionAlgorithm(Algorithm):
-    def process_case(self, *, idx, case):
-        # Load and test the image for this case
-        input_image, input_image_file_path = self._load_input_image(case=case)
-
-        # Detect and score candidates
-        scored_candidates = self.predict(input_image=input_image)
-
-        # Write resulting candidates to result.json for this case
-        return {
-            "outputs": [
-                dict(type="candidates", data=scored_candidates.to_dict())
-            ],
-            "inputs": [
-                dict(type="metaio_image", filename=input_image_file_path.name)
-            ],
-            "error_messages": [],
-        }
-
-    @abstractmethod
-    def predict(self, *, input_image: SimpleITK.Image) -> DataFrame:
-        raise NotImplementedError()
-
-    @staticmethod
-    def _serialize_candidates(
-        *,
-        candidates: Iterable[Tuple[float, ...]],
-        candidate_scores: List[Any],
-        ref_image: SimpleITK.Image,
-    ) -> List[Dict]:
-        data = []
-        for coord, score in zip(candidates, candidate_scores):
-            world_coords = ref_image.TransformContinuousIndexToPhysicalPoint(
-                [c for c in reversed(coord)]
-            )
-            coord_data = {
-                f"coord{k}": v for k, v in zip(["X", "Y", "Z"], world_coords)
-            }
-            coord_data.update({"score": score})
-            data.append(coord_data)
-        return data
-
-
-class SegmentationAlgorithm(Algorithm):
-    def process_case(self, *, idx, case):
-        # Load and test the image for this case
-        input_image, input_image_file_path = self._load_input_image(case=case)
-
-        # Segment nodule candidates
-        segmented_nodules = self.predict(input_image=input_image)
-
-        # Write resulting segmentation to output location
-        segmentation_path = self._output_path / input_image_file_path.name
-        if not self._output_path.exists():
-            self._output_path.mkdir()
-        SimpleITK.WriteImage(segmented_nodules, str(segmentation_path), True)
-
-        # Write segmentation file path to result.json for this case
-        return {
-            "outputs": [
-                dict(type="metaio_image", filename=segmentation_path.name)
-            ],
-            "inputs": [
-                dict(type="metaio_image", filename=input_image_file_path.name)
-            ],
-            "error_messages": [],
-        }
-
-    @abstractmethod
-    def predict(self, *, input_image: SimpleITK.Image) -> SimpleITK.Image:
-        raise NotImplementedError()
-
-
-class ClassificationAlgorithm(Algorithm):
-    def process_case(self, *, idx, case):
-        # Load and test the image for this case
-        input_image, input_image_file_path = self._load_input_image(case=case)
-
-        # Classify input_image image
-        results = self.predict(input_image=input_image)
-
-        # Test classification output
-        if not isinstance(results, dict):
-            raise ValueError("Exepected a dictionary as output")
-
-        # Write resulting classification to result.json for this case
-        return {
-            "outputs": [results],
-            "inputs": [
-                dict(type="metaio_image", filename=input_image_file_path.name)
-            ],
-            "error_messages": [],
-        }
-
-    @abstractmethod
-    def predict(self, *, input_image: SimpleITK.Image) -> Dict:
-        raise NotImplementedError()
-
-
-class BaseEvaluation(ABC):
-    def __init__(
-        self,
-        *,
-        ground_truth_path: Path = DEFAULT_GROUND_TRUTH_PATH,
-        predictions_path: Path = DEFAULT_INPUT_PATH,
-        file_sorter_key: Callable = first_int_in_filename_key,
-        file_loader: FileLoader,
-        validators: Tuple[DataFrameValidator, ...],
-        join_key: Optional[str] = None,
-        aggregates: Optional[Set[str]] = None,
-        output_file: PathLike = DEFAULT_EVALUATION_OUTPUT_FILE_PATH,
-    ):
-        """
-        The base class for all evaluations. Sets the environment and controls
-        the flow of the evaluation once `evaluate` is called.
-
-
-        Parameters
-        ----------
-        ground_truth_path
-            The path in the container where the ground truth will be loaded
-            from
-        predictions_path
-            The path in the container where the submission will be loaded from
-        file_sorter_key
-            A function that determines how files are sorted and matched
-            together
-        file_loader
-            The loader that will be used to get all files
-        validators
-            A tuple containing all the validators that will be used on the
-            loaded data
-        join_key
-            The column that will be used to join the predictions and ground
-            truth tables
-        aggregates
-            The set of aggregates that will be calculated by
-            `pandas.DataFrame.describe`
-        output_file
-            The path to the location where the results will be written
-        """
-        if aggregates is None:
-            aggregates = {
-                "mean",
-                "std",
-                "min",
-                "max",
-                "25%",
-                "50%",
-                "75%",
-                "count",
-                "uniq",
-                "freq",
-            }
-
-        self._ground_truth_path = ground_truth_path
-        self._predictions_path = predictions_path
-        self._file_sorter_key = file_sorter_key
-        self._file_loader = file_loader
-        self._validators = validators
-        self._join_key = join_key
-        self._aggregates = aggregates
-        self._output_file = output_file
-
-        self._ground_truth_cases = DataFrame()
-        self._predictions_cases = DataFrame()
-
-        self._cases = DataFrame()
-
-        self._case_results = DataFrame()
-        self._aggregate_results: Dict[str, Union[float, int, str, None]] = {}
-
-        super().__init__()
-
-        if isinstance(self._file_loader, CSVLoader) and self._join_key is None:
-            raise ConfigurationError(
-                f"You must set a `join_key` when using {self._file_loader}."
-            )
-
-    @property
-    def _metrics(self) -> Dict:
-        """Returns the calculated case and aggregate results"""
-        return {
-            "case": self._case_results.to_dict(),
-            "aggregates": self._aggregate_results,
-        }
-
-    def evaluate(self):
-        self.load()
-        self.validate()
-        self.merge_ground_truth_and_predictions()
-        self.cross_validate()
-        self.score()
-        self.save()
-
-    def load(self):
-        self._ground_truth_cases = self._load_cases(
-            folder=self._ground_truth_path
-        )
-        self._predictions_cases = self._load_cases(
-            folder=self._predictions_path
-        )
-
-    def _load_cases(self, *, folder: Path) -> DataFrame:
-        cases = None
-
-        for f in sorted(folder.glob("**/*"), key=self._file_sorter_key):
-            try:
-                new_cases = self._file_loader.load(fname=f)
-            except FileLoaderError:
-                logger.warning(
-                    f"Could not load {f.name} using {self._file_loader}."
-                )
-            else:
-                if cases is None:
-                    cases = new_cases
-                else:
-                    cases += new_cases
-
-        if cases is None:
-            raise FileLoaderError(
-                f"Could not load any files in {folder} with "
-                f"{self._file_loader}."
-            )
-
-        return DataFrame(cases)
-
-    def validate(self):
-        """Validates each dataframe separately"""
-        self._validate_data_frame(df=self._ground_truth_cases)
-        self._validate_data_frame(df=self._predictions_cases)
-
-    def _validate_data_frame(self, *, df: DataFrame):
-        for validator in self._validators:
-            validator.validate(df=df)
-
-    @abstractmethod
-    def merge_ground_truth_and_predictions(self):
-        pass
-
-    @abstractmethod
-    def cross_validate(self):
-        """Validates both dataframes"""
-        pass
-
-    def _raise_missing_predictions_error(self, *, missing=None):
-        if missing is not None:
-            message = (
-                "Predictions missing: you did not submit predictions for "
-                f"{missing}. Please try again."
-            )
-        else:
-            message = (
-                "Predictions missing: you did not submit enough predictions, "
-                "please try again."
-            )
-
-        raise ValidationError(message)
-
-    def _raise_extra_predictions_error(self, *, extra=None):
-        if extra is not None:
-            message = (
-                "Too many predictions: we do not have the ground truth data "
-                f"for {extra}. Please try again."
-            )
-        else:
-            message = (
-                "Too many predictions: you submitted too many predictions, "
-                "please try again."
-            )
-
-        raise ValidationError(message)
-
-    @abstractmethod
-    def score(self):
-        pass
-
-    # noinspection PyUnusedLocal
-    def score_case(self, *, idx: int, case: DataFrame) -> Dict:
-        return {}
-
-    def score_aggregates(self) -> Dict:
-        aggregate_results = {}
-
-        for col in self._case_results.columns:
-            aggregate_results[col] = self.aggregate_series(
-                series=self._case_results[col]
-            )
-
-        return aggregate_results
-
-    def aggregate_series(self, *, series: Series) -> Dict:
-        summary = series.describe()
-        valid_keys = [a for a in self._aggregates if a in summary]
-
-        series_summary = {}
-
-        for k in valid_keys:
-            value = summary[k]
-
-            # % in keys could cause problems when looking up values later
-            key = k.replace("%", "pc")
-
-            try:
-                json.dumps(value)
-            except TypeError:
-                logger.warning(
-                    f"Could not serialize {key}: {value} as json, "
-                    f"so converting {value} to int."
-                )
-                value = int(value)
-
-            series_summary[key] = value
-
-        return series_summary
-
-    def save(self):
-        with open(self._output_file, "w") as f:
-            f.write(json.dumps(self._metrics))
-
-
-class ClassificationEvaluation(BaseEvaluation):
-    """
-    ClassificationEvaluations have the same number of predictions as the
-    number of ground truth cases. These can be things like, what is the
-    stage of this case, or segment some things in this case.
-    """
-
-    def merge_ground_truth_and_predictions(self):
-        if self._join_key:
-            kwargs = {"on": self._join_key}
-        else:
-            kwargs = {"left_index": True, "right_index": True}
-
-        self._cases = merge(
-            left=self._ground_truth_cases,
-            right=self._predictions_cases,
-            indicator=True,
-            how="outer",
-            suffixes=("_ground_truth", "_prediction"),
-            **kwargs,
-        )
-
-    def cross_validate(self):
-        missing = [
-            p for _, p in self._cases.iterrows() if p["_merge"] == "left_only"
-        ]
-
-        if missing:
-            if self._join_key:
-                missing = [p[self._join_key] for p in missing]
-            self._raise_missing_predictions_error(missing=missing)
-
-        extra = [
-            p for _, p in self._cases.iterrows() if p["_merge"] == "right_only"
-        ]
-
-        if extra:
-            if self._join_key:
-                extra = [p[self._join_key] for p in extra]
-            self._raise_extra_predictions_error(extra=extra)
-
-    def score(self):
-        self._case_results = DataFrame()
-        for idx, case in self._cases.iterrows():
-            self._case_results = concat(
-                [
-                    self._case_results,
-                    DataFrame.from_records(
-                        [self.score_case(idx=idx, case=case)]
-                    ),
-                ],
-                ignore_index=True,
-            )
-        self._aggregate_results = self.score_aggregates()
-
-
-class Evaluation(ClassificationEvaluation):
-    """
-    Legacy class, you should use ClassificationEvaluation instead.
-    """
-
-    def __init__(self, *args, **kwargs):
-        warn(
-            (
-                "The Evaluation class is deprecated, "
-                "please use ClassificationEvaluation instead"
-            ),
-            DeprecationWarning,
-        )
-        super().__init__(*args, **kwargs)
-
-
-class DetectionEvaluation(BaseEvaluation):
-    """
-    DetectionEvaluations have a different number of predictions from the
-    number of ground truth annotations. An example would be detecting lung
-    nodules in a CT volume, or malignant cells in a pathology slide.
-    """
-
-    def __init__(self, *args, detection_radius, detection_threshold, **kwargs):
-        super().__init__(*args, **kwargs)
-        self._detection_radius = detection_radius
-        self._detection_threshold = detection_threshold
-
-    def merge_ground_truth_and_predictions(self):
-        self._cases = concat(
-            [self._ground_truth_cases, self._predictions_cases],
-            keys=["ground_truth", "predictions"],
-        )
-
-    def cross_validate(self):
-        expected_keys = set(self._ground_truth_cases[self._join_key])
-        submitted_keys = set(self._predictions_cases[self._join_key])
-
-        missing = expected_keys - submitted_keys
-        if missing:
-            self._raise_missing_predictions_error(missing=missing)
-
-        extra = submitted_keys - expected_keys
-        if extra:
-            self._raise_extra_predictions_error(extra=extra)
-
-    def _raise_extra_predictions_error(self, *, extra=None):
-        """In detection challenges extra predictions are ok"""
-        warn(f"There are extra predictions for cases: {extra}.")
-
-    def _raise_missing_predictions_error(self, *, missing=None):
-        """In detection challenges missing predictions are ok"""
-        warn(f"Could not find predictions for cases: {missing}.")
-
-    def score(self):
-        cases = set(self._ground_truth_cases[self._join_key])
-        cases |= set(self._predictions_cases[self._join_key])
-
-        self._case_results = DataFrame()
-
-        for idx, case in enumerate(cases):
-            self._case_results = concat(
-                [
-                    self._case_results,
-                    DataFrame.from_records(
-                        [
-                            self.score_case(
-                                idx=idx,
-                                case=self._cases.loc[
-                                    self._cases[self._join_key] == case
-                                ],
-                            )
-                        ]
-                    ),
-                ],
-                ignore_index=True,
-            )
-        self._aggregate_results = self.score_aggregates()
-
-    def score_case(self, *, idx, case):
-        score = score_detection(
-            ground_truth=self.get_points(case=case, key="ground_truth"),
-            predictions=self.get_points(case=case, key="predictions"),
-            radius=self._detection_radius,
-        )
-
-        # Add the case id to the score
-        output = score._asdict()
-        output.update({self._join_key: case[self._join_key][0]})
-
-        return output
-
-    def get_points(
-        self, *, case, key: str
-    ) -> List[Tuple[Union[int, float], Union[int, float]]]:
-        raise NotImplementedError
-
-    def score_aggregates(self):
-        aggregate_results = super().score_aggregates()
-
-        totals = self._case_results.sum()
-
-        for s in ["true_positives", "false_positives", "false_negatives"]:
-            aggregate_results[s]["sum"] = int(totals[s])
-
-        tp = aggregate_results["true_positives"]["sum"]
-        fp = aggregate_results["false_positives"]["sum"]
-        fn = aggregate_results["false_negatives"]["sum"]
-
-        aggregate_results["precision"] = tp / (tp + fp)
-        aggregate_results["recall"] = tp / (tp + fn)
-        aggregate_results["f1_score"] = 2 * tp / ((2 * tp) + fp + fn)
-
-        return aggregate_results
diff --git a/evalutils/exceptions.py b/evalutils/exceptions.py
deleted file mode 100644
index 7bcbe86..0000000
--- a/evalutils/exceptions.py
+++ /dev/null
@@ -1,14 +0,0 @@
-class EvalUtilsError(Exception):
-    pass
-
-
-class FileLoaderError(EvalUtilsError):
-    pass
-
-
-class ValidationError(EvalUtilsError):
-    pass
-
-
-class ConfigurationError(EvalUtilsError):
-    pass
diff --git a/evalutils/io.py b/evalutils/io.py
deleted file mode 100644
index 9cbd8df..0000000
--- a/evalutils/io.py
+++ /dev/null
@@ -1,170 +0,0 @@
-import logging
-import re
-from abc import ABC, abstractmethod
-from pathlib import Path
-from typing import Dict, List
-
-from imageio import get_reader
-from pandas import read_csv
-from pandas.errors import EmptyDataError, ParserError
-from SimpleITK import GetArrayFromImage, ReadImage
-
-from .exceptions import FileLoaderError
-
-logger = logging.getLogger(__name__)
-
-
-def get_first_int_in(s: str) -> int:
-    """
-    Gets the first integer in a string.
-
-    Parameters
-    ----------
-    s
-        The string to search for an int
-
-    Returns
-    -------
-        The first integer found in the string
-
-    Raises
-    ------
-    AttributeError
-        If there is not an int contained in the string
-
-    """
-    r = re.compile(r"\D*((?:\d+\.?)+)\D*")
-    m = r.search(s)
-
-    if m is not None:
-        return int(m.group(1).replace(".", ""))
-    else:
-        raise AttributeError(f"No int found in {s}")
-
-
-def first_int_in_filename_key(fname: Path) -> str:
-    try:
-        return f"{get_first_int_in(fname.stem):>64}"
-    except AttributeError:
-        logger.warning(f"Could not find an int in the string {fname.stem!r}.")
-        return fname.stem
-
-
-class FileLoader(ABC):
-    @abstractmethod
-    def load(self, *, fname: Path) -> List[Dict]:
-        """
-        Tries to load the file given by the path fname.
-
-        Notes
-        -----
-        For this to work with the validators you must:
-
-            If you load an image it must save the hash in the `hash` column
-
-            If you reference a Path it must be saved in the `path` column
-
-
-        Parameters
-        ----------
-        fname
-            The file that the loader will try to load
-
-        Returns
-        -------
-            A list containing all of the cases in this file
-
-        Raises
-        ------
-        FileLoaderError
-            If a file cannot be loaded as the specified type
-
-        """
-        raise FileLoaderError
-
-
-class ImageLoader(FileLoader):
-    """
-    A specialised file loader for images. As images are large they will not
-    all be loaded into memory, so score_case needs to load them again later
-    via load_image.
-    """
-
-    def load(self, *, fname: Path):
-        try:
-            img = self.load_image(fname)
-        except (ValueError, RuntimeError):
-            raise FileLoaderError(
-                f"Could not load {fname} using {self.__class__.__qualname__}."
-            )
-        return [{"hash": self.hash_image(img), "path": fname}]
-
-    @staticmethod
-    def load_image(fname: Path):
-        """
-        Loads the image
-
-        Parameters
-        ----------
-        fname
-            The path that the loader will try to load
-
-        Returns
-        -------
-            The image
-        """
-        raise NotImplementedError
-
-    @staticmethod
-    def hash_image(image) -> int:
-        """
-        Generates a hash of the image
-
-        Parameters
-        ----------
-        image
-            The image to hash
-
-        Returns
-        -------
-            The hash of the image
-
-        """
-        raise NotImplementedError
-
-
-class ImageIOLoader(ImageLoader):
-    @staticmethod
-    def load_image(fname):
-        with open(fname, "rb") as f:
-            with get_reader(f, mode="i", pilmode="F") as r:
-                return r.get_data(0)
-
-    @staticmethod
-    def hash_image(image):
-        return hash(image.tobytes())
-
-
-class SimpleITKLoader(ImageLoader):
-    @staticmethod
-    def load_image(fname):
-        return ReadImage(str(fname))
-
-    @staticmethod
-    def hash_image(image):
-        return hash(GetArrayFromImage(image).tobytes())
-
-
-class CSVLoader(FileLoader):
-    def load(self, *, fname: Path):
-        try:
-            return read_csv(
-                fname, skipinitialspace=True, encoding="utf-8"
-            ).to_dict(orient="records")
-        except UnicodeDecodeError:
-            raise FileLoaderError(f"Could not load {fname} using {__name__}.")
-        except (ParserError, EmptyDataError):
-            raise ValueError(
-                f"CSV file could not be loaded: we could not load "
-                f"{fname.name} using `pandas.read_csv`."
-            )
diff --git a/evalutils/roc.py b/evalutils/roc.py
index db87463..9a08e4d 100644
--- a/evalutils/roc.py
+++ b/evalutils/roc.py
@@ -1,4 +1,4 @@
-from typing import List, NamedTuple, Tuple
+from typing import NamedTuple
 
 import numpy as np
 from numpy import ndarray
@@ -55,9 +55,9 @@ def get_bootstrapped_roc_ci_curves(
     """
 
     rng_seed = 40  # control reproducibility
-    bootstrapped_az_scores: List[float] = []
+    bootstrapped_az_scores: list[float] = []
 
-    tprs_list: List[ndarray] = []
+    tprs_list: list[ndarray] = []
     base_fpr = np.linspace(0, 1, 101)
     rng = np.random.RandomState(rng_seed)
 
@@ -114,7 +114,7 @@ def get_bootstrapped_roc_ci_curves(
 
 
 def average_roc_curves(
-    roc_curves: List[BootstrappedROCCICurves], bins: int = 200
+    roc_curves: list[BootstrappedROCCICurves], bins: int = 200
 ) -> BootstrappedROCCICurves:
     """
     Averages ROC curves using vertical averaging (fixed FP rates),
@@ -237,8 +237,8 @@ def get_bootstrapped_ci_point_error(
         The fpr vals (one per ROC point) representing the highest val in CI
     """
     rng_seed = 40  # control reproducibility
-    tprs_list: List[ndarray] = []
-    fprs_list: List[ndarray] = []
+    tprs_list: list[ndarray] = []
+    fprs_list: list[ndarray] = []
     rng = np.random.RandomState(rng_seed)
 
     num_possible_scores = len(np.unique(y_score))
@@ -302,7 +302,7 @@ def get_bootstrapped_ci_point_error(
 
 def _get_confidence_intervals(
     *, n_bootstraps: int, one_sided_ci: float, points_array
-) -> Tuple[ndarray, ndarray]:
+) -> tuple[ndarray, ndarray]:
     ci_upper = []
     ci_lower = []
 
diff --git a/evalutils/scorers.py b/evalutils/scorers.py
index 897240a..4b2b2fd 100644
--- a/evalutils/scorers.py
+++ b/evalutils/scorers.py
@@ -1,5 +1,4 @@
 from collections import namedtuple
-from typing import List, Tuple
 
 from numpy import array
 from sklearn.neighbors import BallTree
@@ -11,8 +10,8 @@
 
 def score_detection(
     *,
-    ground_truth: List[Tuple[float, ...]],
-    predictions: List[Tuple[float, ...]],
+    ground_truth: list[tuple[float, ...]],
+    predictions: list[tuple[float, ...]],
     radius: float = 1.0,
 ) -> DetectionScore:
     """
@@ -97,10 +96,10 @@ def score_detection(
 
 def find_hits_for_targets(
     *,
-    targets: List[Tuple[float, ...]],
-    predictions: List[Tuple[float, ...]],
+    targets: list[tuple[float, ...]],
+    predictions: list[tuple[float, ...]],
     radius: float,
-) -> List[Tuple[int, ...]]:
+) -> list[tuple[int, ...]]:
     """
     Generates a list of the predicted points that are within a radius r of the
     targets. The indicies are returned in sorted order, from closest to
diff --git a/evalutils/stats.py b/evalutils/stats.py
index 37cc6b0..144f9f9 100644
--- a/evalutils/stats.py
+++ b/evalutils/stats.py
@@ -1,195 +1,22 @@
-import gc
 from collections import namedtuple
-from typing import Callable, List, Optional, Tuple, Union
+from typing import Optional, Union
 
 import numpy as np
 from numpy import ndarray
-from scipy.ndimage import binary_erosion, convolve, generate_binary_structure
+from scipy.ndimage import (
+    binary_erosion,
+    convolve,
+    distance_transform_edt,
+    generate_binary_structure,
+)
 
 VOXELSPACING_TYPE = Optional[
-    Union[Tuple[Union[float, int], ...], List[Union[float, int]], float, int]
+    Union[tuple[Union[float, int], ...], list[Union[float, int]], float, int]
 ]
 
 
-def distance_transform_edt_float32(  # noqa: C901
-    input,
-    sampling=None,
-    return_distances=True,
-    return_indices=False,
-    distances=None,
-    indices=None,
-):
-    """Memory efficient version of scipy.ndimage.distance_transform_edt
-
-    The same as scipy.ndimage.distance_transform_edt but
-    using float32 and better memory cleaning internally.
-
-    In addition to the distance transform, the feature transform can
-    be calculated. In this case the index of the closest background
-    element is returned along the first axis of the result.
-
-    Parameters
-    ----------
-    input
-        Input data to transform. Can be any type but will be converted
-        into binary: 1 wherever input equates to True, 0 elsewhere.
-    sampling
-        Spacing of elements along each dimension. If a sequence, must be of
-        length equal to the input rank; if a single number, this is used for
-        all axes. If not specified, a grid spacing of unity is implied.
-    return_distances
-        Whether to return distance matrix. At least one of
-        return_distances/return_indices must be True. Default is True.
-    return_indices
-        Whether to return indices matrix. Default is False.
-    distances
-        Used for output of distance array, must be of type float64.
-    indices
-        Used for output of indices, must be of type int32.
-
-    Returns
-    -------
-    distance_transform_edt
-        Either distance matrix, index matrix, or a list of the two,
-        depending on ``return_x`` flags and ``distance`` and ``indices``
-        input parameters.
-
-    Notes
-    -----
-
-    The euclidean distance transform gives values of the euclidean
-    distance:
-
-    .. code-block:: console
-
-                      n
-        y_i = sqrt(sum (x[i]-b[i])**2)
-                      i
-
-    where b[i] is the background point (value 0) with the smallest
-    Euclidean distance to input points x[i], and n is the
-    number of dimensions.
-
-    Copyright (C) 2003-2005 Peter J. Verveer
-
-    Redistribution and use in source and binary forms, with or without
-    modification, are permitted provided that the following conditions
-    are met:
-
-    1. Redistributions of source code must retain the above copyright
-       notice, this list of conditions and the following disclaimer.
-
-    2. Redistributions in binary form must reproduce the above
-       copyright notice, this list of conditions and the following
-       disclaimer in the documentation and/or other materials provided
-       with the distribution.
-
-    3. The name of the author may not be used to endorse or promote
-       products derived from this software without specific prior
-       written permission.
-
-    THIS SOFTWARE IS PROVIDED BY THE AUTHOR ''AS IS'' AND ANY EXPRESS
-    OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-    ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
-    DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-    DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
-    GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-    INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
-    WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
-    NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-    SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-    """
-    from scipy.ndimage import _nd_image, _ni_support
-
-    if (not return_distances) and (not return_indices):
-        msg = "at least one of distances/indices must be specified"
-        raise RuntimeError(msg)
-
-    ft_inplace = isinstance(indices, np.ndarray)
-    dt_inplace = isinstance(distances, np.ndarray)
-
-    # calculate the feature transform
-    input = np.atleast_1d(np.where(input, 1, 0).astype(np.int8))
-
-    garbage_collect = gc.collect if input.nbytes > 100e6 else lambda: None
-    garbage_collect()
-
-    input = input.astype(np.int32)
-    garbage_collect()
-
-    if sampling is not None:
-        sampling = _ni_support._normalize_sequence(sampling, input.ndim)
-        sampling = np.asarray(sampling, dtype=np.float64)
-        if not sampling.flags.contiguous:
-            sampling = sampling.copy()
-
-    if ft_inplace:
-        ft = indices
-        if ft.shape != (input.ndim,) + input.shape:
-            raise RuntimeError("indices has wrong shape")
-        if ft.dtype.type != np.int32:
-            raise RuntimeError("indices must be of int32 type")
-    else:
-        ft = np.zeros((input.ndim,) + input.shape, dtype=np.int32)
-
-    _nd_image.euclidean_feature_transform(input, sampling, ft)
-    input_shape = input.shape
-
-    del input
-    garbage_collect()
-
-    # if requested, calculate the distance transform
-    if return_distances:
-        # dt = ft - np.indices(input.shape, dtype=ft.dtype)
-        # Paul K. Gerke: Save a lot of memory by doing the operation
-        # column-wise and in-pace.
-
-        if return_indices:
-            dt = ft.copy()
-        else:
-            dt = ft
-            del ft
-
-        c_indices = np.indices((1,) + input_shape[1:], dtype=dt.dtype)
-        for c in range(input_shape[0]):
-            dt[:, c : (c + 1)] -= c_indices  # noqa: E203
-            c_indices[0] += 1
-
-        dt = dt.astype(np.float32, copy=False)
-        if sampling is not None:
-            for ii in range(len(sampling)):
-                dt[ii, ...] *= sampling[ii]
-        np.multiply(dt, dt, dt)
-        if dt_inplace:
-            dt = np.add.reduce(dt, axis=0)
-            if distances.shape != dt.shape:
-                raise RuntimeError("indices has wrong shape")
-            if distances.dtype.type != np.float32:
-                raise RuntimeError("indices must be of float32 type")
-            np.sqrt(dt, distances)
-        else:
-            dt = np.add.reduce(dt, axis=0)
-            dt = np.sqrt(dt)
-
-    # construct and return the result
-    result = []
-    if return_distances and not dt_inplace:
-        result.append(dt)
-    if return_indices and not ft_inplace:
-        result.append(ft)
-
-    if len(result) == 2:
-        return tuple(result)
-    elif len(result) == 1:
-        return result[0]
-    else:
-        return None
-
-
 def calculate_confusion_matrix(
-    y_true: ndarray, y_pred: ndarray, labels: List[int]
+    y_true: ndarray, y_pred: ndarray, labels: list[int]
 ) -> ndarray:
     """
     Efficient confusion matrix calculation, based on sklearn interface
@@ -340,7 +167,6 @@ def __surface_distances(
     s2: ndarray,
     voxelspacing: VOXELSPACING_TYPE = None,
     connectivity: int = 1,
-    edt_method: Callable = distance_transform_edt_float32,
 ) -> ndarray:
     """
     Computes set of surface distances.
@@ -367,11 +193,6 @@ def __surface_distances(
         of the binary objects. This value is passed to
         `scipy.ndimage.generate_binary_structure` and should usually
         be :math:`> 1`.
-    edt_method
-        Method used for computing the euclidean distance transform. By default
-        it uses a variant on the `scipy.ndimage.distance_transform_edt`
-        method that uses float32 data to reduce memory costs at the cost of
-        some additional compute time.
 
     Returns
     -------
@@ -392,7 +213,7 @@ def __surface_distances(
         s2_b = s2_b & ~binary_erosion(s2_b, structure=footprint, iterations=1)
         s1_b = s1_b & ~binary_erosion(s1_b, structure=footprint, iterations=1)
 
-    return edt_method(~s2_b, sampling=voxelspacing)[s1_b]
+    return distance_transform_edt(~s2_b, sampling=voxelspacing)[s1_b]
 
 
 def hausdorff_distance(
@@ -400,7 +221,6 @@ def hausdorff_distance(
     s2: ndarray,
     voxelspacing: VOXELSPACING_TYPE = None,
     connectivity: int = 1,
-    edt_method: Callable = distance_transform_edt_float32,
 ) -> float:
     """
     Computes the (symmetric) Hausdorff Distance (HD) between the binary objects
@@ -425,11 +245,6 @@ def hausdorff_distance(
         of the binary objects. This value is passed to
         `scipy.ndimage.generate_binary_structure` and should usually
         be :math:`> 1`.
-    edt_method
-        Method used for computing the euclidean distance transform. By default
-        it uses a variant on the `scipy.ndimage.distance_transform_edt`
-        method that uses float32 data to reduce memory costs at the cost of
-        some additional compute time.
 
     Returns
     -------
@@ -443,12 +258,8 @@ def hausdorff_distance(
     Implementation inspired by medpy.metric.binary
     http://pythonhosted.org/MedPy/_modules/medpy/metric/binary.html
     """
-    s1_dist = __surface_distances(
-        s1, s2, voxelspacing, connectivity, edt_method=edt_method
-    )
-    s2_dist = __surface_distances(
-        s2, s1, voxelspacing, connectivity, edt_method=edt_method
-    )
+    s1_dist = __surface_distances(s1, s2, voxelspacing, connectivity)
+    s2_dist = __surface_distances(s2, s1, voxelspacing, connectivity)
 
     return max(s1_dist.max(), s2_dist.max())
 
@@ -459,7 +270,6 @@ def percentile_hausdorff_distance(
     percentile: Union[int, float] = 0.95,
     voxelspacing: VOXELSPACING_TYPE = None,
     connectivity: int = 1,
-    edt_method: Callable = distance_transform_edt_float32,
 ) -> float:
     """
     Nth Percentile Hausdorff Distance.
@@ -489,11 +299,6 @@ def percentile_hausdorff_distance(
         of the binary objects. This value is passed to
         `scipy.ndimage.generate_binary_structure` and should usually
         be :math:`> 1`.
-    edt_method
-        Method used for computing the euclidean distance transform. By default
-        it uses a variant on the `scipy.ndimage.distance_transform_edt`
-        method that uses float32 data to reduce memory costs at the cost of
-        some additional compute time.
 
     Returns
     -------
@@ -511,12 +316,8 @@ def percentile_hausdorff_distance(
     -----
     This is a real metric.
     """
-    s1_dist = __surface_distances(
-        s1, s2, voxelspacing, connectivity, edt_method=edt_method
-    )
-    s2_dist = __surface_distances(
-        s2, s1, voxelspacing, connectivity, edt_method=edt_method
-    )
+    s1_dist = __surface_distances(s1, s2, voxelspacing, connectivity)
+    s2_dist = __surface_distances(s2, s1, voxelspacing, connectivity)
 
     s1_dist.sort()
     s2_dist.sort()
@@ -532,7 +333,6 @@ def modified_hausdorff_distance(
     s2: ndarray,
     voxelspacing: VOXELSPACING_TYPE = None,
     connectivity: int = 1,
-    edt_method: Callable = distance_transform_edt_float32,
 ) -> float:
     """
     Computes the (symmetric) Modified Hausdorff Distance (MHD) between the
@@ -557,11 +357,6 @@ def modified_hausdorff_distance(
         of the binary objects. This value is passed to
         `scipy.ndimage.generate_binary_structure` and should usually
         be :math:`> 1`.
-    edt_method
-        Method used for computing the euclidean distance transform. By default
-        it uses a variant on the `scipy.ndimage.distance_transform_edt`
-        method that uses float32 data to reduce memory costs at the cost of
-        some additional compute time.
 
     Returns
     -------
@@ -574,12 +369,8 @@ def modified_hausdorff_distance(
     -----
     This is a real metric.
     """
-    s1_dist = __surface_distances(
-        s1, s2, voxelspacing, connectivity, edt_method=edt_method
-    )
-    s2_dist = __surface_distances(
-        s2, s1, voxelspacing, connectivity, edt_method=edt_method
-    )
+    s1_dist = __surface_distances(s1, s2, voxelspacing, connectivity)
+    s2_dist = __surface_distances(s2, s1, voxelspacing, connectivity)
 
     return max(s1_dist.mean(), s2_dist.mean())
 
@@ -668,7 +459,6 @@ def __directed_contour_distances(
     s1: ndarray,
     s2: ndarray,
     voxelspacing: VOXELSPACING_TYPE = None,
-    edt_method: Callable = distance_transform_edt_float32,
 ) -> ndarray:
     """
     Computes set of surface contour distances.
@@ -693,11 +483,6 @@ def __directed_contour_distances(
         along each dimension. If a sequence, must be of length equal to
         the input rank; if a single number, this is used for all axes. If
         not specified, a grid spacing of unity is implied.
-    edt_method
-        Method used for computing the euclidean distance transform. By default
-        it uses a variant on the `scipy.ndimage.distance_transform_edt`
-        method that uses float32 data to reduce memory costs at the cost of
-        some additional compute time.
 
     Returns
     -------
@@ -720,7 +505,7 @@ def __directed_contour_distances(
     # all elements in neighborhood are fully checked! equals np.ones((3,3,3))
     # for s1.ndim == 3
     footprint = generate_binary_structure(s1.ndim, s1.ndim)
-    df = edt_method(~s2_b, sampling=voxelspacing)
+    df = distance_transform_edt(~s2_b, sampling=voxelspacing)
 
     # generate mask for elements not entirly enclosed by mask s1_b
     # (contours & non-zero elements)
@@ -737,7 +522,6 @@ def mean_contour_distance(
     s1: ndarray,
     s2: ndarray,
     voxelspacing: VOXELSPACING_TYPE = None,
-    edt_method: Callable = distance_transform_edt_float32,
 ) -> float:
     """
     Computes the (symmetric) Mean Contour Distance between the binary objects
@@ -757,11 +541,6 @@ def mean_contour_distance(
         along each dimension. If a sequence, must be of length equal to
         the input rank; if a single number, this is used for all axes. If
         not specified, a grid spacing of unity is implied.
-    edt_method
-        Method used for computing the euclidean distance transform. By default
-        it uses a variant on the `scipy.ndimage.distance_transform_edt`
-        method that uses float32 data to reduce memory costs at the cost of
-        some additional compute time.
 
     Returns
     -------
@@ -773,12 +552,8 @@ def mean_contour_distance(
     -----
     This is a real metric that mimics the ITK MeanContourDistanceFilter.
     """
-    s1_c_dist = __directed_contour_distances(
-        s1, s2, voxelspacing, edt_method=edt_method
-    )
-    s2_c_dist = __directed_contour_distances(
-        s2, s1, voxelspacing, edt_method=edt_method
-    )
+    s1_c_dist = __directed_contour_distances(s1, s2, voxelspacing)
+    s2_c_dist = __directed_contour_distances(s2, s1, voxelspacing)
 
     return max(s1_c_dist.mean(), s2_c_dist.mean())
 
@@ -795,7 +570,6 @@ def hausdorff_distance_measures(
     voxelspacing: VOXELSPACING_TYPE = None,
     connectivity: int = 1,
     percentile: float = 0.95,
-    edt_method: Callable = distance_transform_edt_float32,
 ) -> HausdorffMeasures:
     """
     Returns multiple Hausdorff measures - (hd, modified_hd, percentile_hd)
@@ -822,11 +596,6 @@ def hausdorff_distance_measures(
         be :math:`> 1`.
     percentile
         The percentile at which to calculate the Hausdorff Distance
-    edt_method
-        Method used for computing the euclidean distance transform. By default
-        it uses a variant on the `scipy.ndimage.distance_transform_edt`
-        method that uses float32 data to reduce memory costs at the cost of
-        some additional compute time.
 
     Returns
     -------
@@ -845,8 +614,8 @@ def hausdorff_distance_measures(
         s2_b = s2_b & ~binary_erosion(s2_b, structure=footprint, iterations=1)
         s1_b = s1_b & ~binary_erosion(s1_b, structure=footprint, iterations=1)
 
-    s1_dist = edt_method(~s2_b, sampling=voxelspacing)[s1_b]
-    s2_dist = edt_method(~s1_b, sampling=voxelspacing)[s2_b]
+    s1_dist = distance_transform_edt(~s2_b, sampling=voxelspacing)[s1_b]
+    s2_dist = distance_transform_edt(~s1_b, sampling=voxelspacing)[s2_b]
 
     s1_dist.sort()
     s2_dist.sort()
diff --git a/evalutils/templates/container/__init__.py b/evalutils/templates/container/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/evalutils/templates/container/cookiecutter.json b/evalutils/templates/container/cookiecutter.json
deleted file mode 100644
index 817a4e5..0000000
--- a/evalutils/templates/container/cookiecutter.json
+++ /dev/null
@@ -1,18 +0,0 @@
-{
-    "template_kind": [
-        "Algorithm",
-        "Evaluation"
-    ],
-    "full_project_name": "",
-    "task_kind": [
-        "Classification",
-        "Segmentation",
-        "Detection"
-    ],
-    "package_name": "{{ cookiecutter.full_project_name|replace(' ', '') }}",
-    "evalutils_version": "",
-    "dev_build": 0,
-    "python_major_version": "",
-    "python_minor_version": "",
-    "requirements": {}
-}
diff --git a/evalutils/templates/container/hooks/__init__.py b/evalutils/templates/container/hooks/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/evalutils/templates/container/hooks/post_gen_project.py b/evalutils/templates/container/hooks/post_gen_project.py
deleted file mode 100644
index dda0ea9..0000000
--- a/evalutils/templates/container/hooks/post_gen_project.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import os
-import shutil
-from pathlib import Path
-
-from evalutils.utils import (
-    convert_line_endings,
-    generate_requirements_txt,
-    generate_source_wheel,
-)
-
-TASK_KIND = "{{ cookiecutter.task_kind }}"
-TEMPLATE_KIND = "{{ cookiecutter.template_kind }}"
-IS_DEV_BUILD = int("{{ cookiecutter.dev_build }}") == 1
-
-template_dir = Path(os.getcwd())
-
-templated_files = template_dir.glob("*.j2")
-for f in templated_files:
-    shutil.move(f.name, f.stem)
-
-if TEMPLATE_KIND == "Evaluation":  # noqa: C901
-    shutil.rmtree("algorithm_test")
-    os.remove("process.py")
-    os.rename("evaluation_test", "test")
-
-    def remove_classification_files():
-        os.remove(Path("ground-truth") / "reference.csv")
-        os.remove(Path("test") / "submission.csv")
-
-    def remove_segmentation_files():
-        files = []
-        for ext in ["mhd", "zraw"]:
-            files.extend(Path(".").glob(f"**/*.{ext}"))
-
-        for file in files:
-            os.remove(str(file))
-
-    def remove_detection_files():
-        os.remove(Path("ground-truth") / "detection-reference.csv")
-        os.remove(Path("test") / "detection-submission.csv")
-
-    if TASK_KIND.lower() != "segmentation":
-        remove_segmentation_files()
-
-    if TASK_KIND.lower() != "detection":
-        remove_detection_files()
-
-    if TASK_KIND.lower() != "classification":
-        remove_classification_files()
-
-elif TEMPLATE_KIND == "Algorithm":
-    shutil.rmtree("evaluation_test")
-    shutil.rmtree("ground-truth")
-    os.remove("evaluation.py")
-    os.rename("algorithm_test", "test")
-
-    template_test_dir = template_dir / "test"
-
-    def remove_result_files():
-        for task_kind in ["segmentation", "detection", "classification"]:
-            os.remove(template_test_dir / f"results_{task_kind}.json")
-
-    expected_output_file = (
-        template_test_dir / f"results_{TASK_KIND.lower()}.json"
-    )
-
-    shutil.copy(
-        str(expected_output_file), template_test_dir / "expected_output.json"
-    )
-
-    remove_result_files()
-
-if IS_DEV_BUILD:
-    generate_source_wheel(template_dir / "vendor")
-
-generate_requirements_txt()
-convert_line_endings()
diff --git a/evalutils/templates/container/hooks/pre_gen_project.py b/evalutils/templates/container/hooks/pre_gen_project.py
deleted file mode 100644
index 264caf4..0000000
--- a/evalutils/templates/container/hooks/pre_gen_project.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import re
-
-FORBIDDEN_NAMES = ["evalutils", "pandas", "Evaluation", "Algorithm"]
-
-MODULE_REGEX = r"^[_a-zA-Z][_a-zA-Z0-9]+$"
-
-package_name = "{{ cookiecutter.package_name }}"
-
-if not re.match(MODULE_REGEX, package_name) or package_name in FORBIDDEN_NAMES:
-    print(f"ERROR: {package_name!r} is not a valid Python module name!")
-    exit(1)
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/.dockerignore b/evalutils/templates/container/{{ cookiecutter.package_name }}/.dockerignore
deleted file mode 100644
index 1068f81..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/.dockerignore
+++ /dev/null
@@ -1,3 +0,0 @@
-test/
-.git/
-*.tar.gz
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/.gitattributes.j2 b/evalutils/templates/container/{{ cookiecutter.package_name }}/.gitattributes.j2
deleted file mode 100644
index 545225a..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/.gitattributes.j2
+++ /dev/null
@@ -1,2 +0,0 @@
-ground-truth/* filter=lfs diff=lfs merge=lfs -text
-test/* filter=lfs diff=lfs merge=lfs -text
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/.github/workflows/ci.yml b/evalutils/templates/container/{{ cookiecutter.package_name }}/.github/workflows/ci.yml
deleted file mode 100644
index b30b489..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/.github/workflows/ci.yml
+++ /dev/null
@@ -1,23 +0,0 @@
-name: CI
-
-on: [push, pull_request]
-
-env:
-  PYTHON_VERSION: '{{ cookiecutter.python_major_version }}.{{ cookiecutter.python_minor_version }}'
-
-jobs:
-
-  tests:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Install Python ${{ "{{" }} env.PYTHON_VERSION {{ "}}" }}
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ "{{" }} env.PYTHON_VERSION {{ "}}" }}
-      - uses: actions/checkout@v3
-      - name: Build the containers
-        run: |
-          ./build.sh
-      - name: Run the tests
-        run: |
-          ./test.sh
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/.gitignore b/evalutils/templates/container/{{ cookiecutter.package_name }}/.gitignore
deleted file mode 100644
index 13f175c..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/.gitignore
+++ /dev/null
@@ -1,105 +0,0 @@
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-env/
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-.hypothesis/
-.pytest_cache/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# pyenv
-.python-version
-
-# celery beat schedule file
-celerybeat-schedule
-
-# SageMath parsed files
-*.sage.py
-
-# dotenv
-.env
-
-# virtualenv
-.venv
-venv/
-ENV/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-
-# Pycharm
-.idea/
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/Dockerfile b/evalutils/templates/container/{{ cookiecutter.package_name }}/Dockerfile
deleted file mode 100644
index bfb39de..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/Dockerfile
+++ /dev/null
@@ -1,33 +0,0 @@
-FROM python:{{ cookiecutter.python_major_version }}.{{ cookiecutter.python_minor_version }}-slim
-
-RUN groupadd -r user && useradd -m --no-log-init -r -g user user
-
-RUN mkdir -p /opt/app /input /output \
-    && chown user:user /opt/app /input /output
-
-USER user
-WORKDIR /opt/app
-
-ENV PATH="/home/user/.local/bin:${PATH}"
-
-RUN python -m pip install --user -U pip && python -m pip install --user pip-tools
-
-{% if cookiecutter.dev_build|int -%}
-COPY --chown=user:user vendor /opt/app/vendor
-{%- endif %}
-
-COPY --chown=user:user requirements.txt /opt/app/
-RUN python -m piptools sync requirements.txt
-
-{% if cookiecutter.template_kind == "Evaluation" -%}
-COPY --chown=user:user ground-truth /opt/app/ground-truth
-COPY --chown=user:user evaluation.py /opt/app/
-
-ENTRYPOINT [ "python", "-m", "evaluation" ]
-{%- endif %}
-
-{% if cookiecutter.template_kind == "Algorithm" -%}
-COPY --chown=user:user process.py /opt/app/
-
-ENTRYPOINT [ "python", "-m", "process" ]
-{%- endif %}
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/README.md b/evalutils/templates/container/{{ cookiecutter.package_name }}/README.md
deleted file mode 100644
index 9815a24..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/README.md
+++ /dev/null
@@ -1,6 +0,0 @@
-# {{ cookiecutter.full_project_name }} {{ cookiecutter.template_kind }}
-
-The source code for the {{ cookiecutter.template_kind.lower() }} container for
-{{ cookiecutter.full_project_name }}, generated with
-evalutils version {{ cookiecutter.evalutils_version }}
-using Python {{ cookiecutter.python_major_version }}.{{ cookiecutter.python_minor_version }}.
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/build.sh b/evalutils/templates/container/{{ cookiecutter.package_name }}/build.sh
deleted file mode 100755
index ac80049..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/build.sh
+++ /dev/null
@@ -1,4 +0,0 @@
-#!/usr/bin/env bash
-SCRIPTPATH="$( cd "$(dirname "$0")" ; pwd -P )"
-
-docker build -t {{ cookiecutter.package_name|lower }} "$SCRIPTPATH"
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/evaluation.py.j2 b/evalutils/templates/container/{{ cookiecutter.package_name }}/evaluation.py.j2
deleted file mode 100644
index 3cbea9f..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/evaluation.py.j2
+++ /dev/null
@@ -1,131 +0,0 @@
-{%- if cookiecutter.task_kind == "Classification" -%}
-from sklearn.metrics import accuracy_score
-
-from evalutils import ClassificationEvaluation
-from evalutils.io import CSVLoader
-from evalutils.validators import (
-    NumberOfCasesValidator, ExpectedColumnNamesValidator
-)
-{%- elif cookiecutter.task_kind == "Segmentation" -%}
-import SimpleITK
-
-from evalutils import ClassificationEvaluation
-from evalutils.io import SimpleITKLoader
-from evalutils.validators import (
-    NumberOfCasesValidator, UniquePathIndicesValidator, UniqueImagesValidator
-)
-{%- elif cookiecutter.task_kind == "Detection" -%}
-from evalutils import DetectionEvaluation
-from evalutils.io import CSVLoader
-from evalutils.validators import ExpectedColumnNamesValidator
-{%- endif %}
-
-
-class {{ cookiecutter.package_name|capitalize }}(
-
-{%- if cookiecutter.task_kind == "Detection" -%}
-    DetectionEvaluation
-{%- else -%}
-    ClassificationEvaluation
-{%- endif -%}
-
-):
-{%- if cookiecutter.task_kind == "Classification" %}
-    def __init__(self):
-        super().__init__(
-            file_loader=CSVLoader(),
-            validators=(
-                ExpectedColumnNamesValidator(expected=("case", "class",)),
-                NumberOfCasesValidator(num_cases=8),
-            ),
-            join_key="case",
-        )
-
-    def score_aggregates(self):
-        return {
-            "accuracy_score": accuracy_score(
-                self._cases["class_ground_truth"],
-                self._cases["class_prediction"],
-             ),
-        }
-{% elif cookiecutter.task_kind == "Detection" %}
-    def __init__(self):
-        super().__init__(
-            file_loader=CSVLoader(),
-            validators=(
-                ExpectedColumnNamesValidator(
-                    expected=("image_id", "x", "y", "score")
-                ),
-            ),
-            join_key="image_id",
-            detection_radius=1.0,
-            detection_threshold=0.5,
-        )
-
-    def get_points(self, *, case, key):
-        """
-        Converts the set of ground truth or predictions for this case, into
-        points that represent true positives or predictions
-        """
-        try:
-            points = case.loc[key]
-        except KeyError:
-            # There are no ground truth/prediction points for this case
-            return []
-
-        return [
-            (p["x"], p["y"])
-            for _, p in points.iterrows()
-            if p["score"] > self._detection_threshold
-        ]
-{% elif cookiecutter.task_kind == "Segmentation" %}
-    def __init__(self):
-        super().__init__(
-            file_loader=SimpleITKLoader(),
-            validators=(
-                NumberOfCasesValidator(num_cases=2),
-                UniquePathIndicesValidator(),
-                UniqueImagesValidator(),
-            ),
-        )
-
-    def score_case(self, *, idx, case):
-        gt_path = case["path_ground_truth"]
-        pred_path = case["path_prediction"]
-
-        # Load the images for this case
-        gt = self._file_loader.load_image(gt_path)
-        pred = self._file_loader.load_image(pred_path)
-
-        # Check that they're the right images
-        if (self._file_loader.hash_image(gt) != case["hash_ground_truth"] or
-            self._file_loader.hash_image(pred) != case["hash_prediction"]):
-            raise RuntimeError("Images do not match")
-
-        # Cast to the same type
-        caster = SimpleITK.CastImageFilter()
-        caster.SetOutputPixelType(SimpleITK.sitkUInt8)
-        caster.SetNumberOfThreads(1)
-        gt = caster.Execute(gt)
-        pred = caster.Execute(pred)
-
-        # Score the case
-        overlap_measures = SimpleITK.LabelOverlapMeasuresImageFilter()
-        overlap_measures.SetNumberOfThreads(1)
-        overlap_measures.Execute(gt, pred)
-
-        return {
-            'FalseNegativeError': overlap_measures.GetFalseNegativeError(),
-            'FalsePositiveError': overlap_measures.GetFalsePositiveError(),
-            'MeanOverlap': overlap_measures.GetMeanOverlap(),
-            'UnionOverlap': overlap_measures.GetUnionOverlap(),
-            'VolumeSimilarity': overlap_measures.GetVolumeSimilarity(),
-            'JaccardCoefficient': overlap_measures.GetJaccardCoefficient(),
-            'DiceCoefficient': overlap_measures.GetDiceCoefficient(),
-            'pred_fname': pred_path.name,
-            'gt_fname': gt_path.name,
-        }
-{% endif %}
-
-if __name__ == "__main__":
-    {{ cookiecutter.package_name|capitalize }}().evaluate()
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/export.sh b/evalutils/templates/container/{{ cookiecutter.package_name }}/export.sh
deleted file mode 100755
index aa1d9a5..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/export.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-#!/usr/bin/env bash
-
-./build.sh
-
-docker save {{ cookiecutter.package_name|lower }} | gzip -c > {{ cookiecutter.package_name }}.tar.gz
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/ground-truth/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.mhd b/evalutils/templates/container/{{ cookiecutter.package_name }}/ground-truth/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.mhd
deleted file mode 100644
index b8a8fbf..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/ground-truth/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.mhd
+++ /dev/null
@@ -1,14 +0,0 @@
-ObjectType = Image
-NDims = 3
-BinaryData = True
-BinaryDataByteOrderMSB = False
-CompressedData = True
-CompressedDataSize = 1014889
-TransformMatrix = 1 0 0 0 1 0 0 0 1
-Offset = -161.5 -175 -276.875
-CenterOfRotation = 0 0 0
-AnatomicalOrientation = RAI
-ElementSpacing = 0.683594 0.683594 0.625
-DimSize = 512 512 476
-ElementType = MET_LONG_LONG
-ElementDataFile = 1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.zraw
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/ground-truth/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.zraw b/evalutils/templates/container/{{ cookiecutter.package_name }}/ground-truth/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.zraw
deleted file mode 100644
index f0bf040..0000000
Binary files a/evalutils/templates/container/{{ cookiecutter.package_name }}/ground-truth/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.zraw and /dev/null differ
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/ground-truth/1.2.276.0.28.3.0.14.4.0.20090213134050413.mhd b/evalutils/templates/container/{{ cookiecutter.package_name }}/ground-truth/1.2.276.0.28.3.0.14.4.0.20090213134050413.mhd
deleted file mode 100644
index c8d5e28..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/ground-truth/1.2.276.0.28.3.0.14.4.0.20090213134050413.mhd
+++ /dev/null
@@ -1,14 +0,0 @@
-ObjectType = Image
-NDims = 3
-BinaryData = True
-BinaryDataByteOrderMSB = False
-CompressedData = True
-CompressedDataSize = 806220
-TransformMatrix = 1 0 0 0 1 0 0 0 1
-Offset = -163.672 -317.672 -237.9
-CenterOfRotation = 0 0 0
-AnatomicalOrientation = RAI
-ElementSpacing = 0.65625 0.65625 0.8
-DimSize = 512 512 381
-ElementType = MET_LONG_LONG
-ElementDataFile = 1.2.276.0.28.3.0.14.4.0.20090213134050413.zraw
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/ground-truth/1.2.276.0.28.3.0.14.4.0.20090213134050413.zraw b/evalutils/templates/container/{{ cookiecutter.package_name }}/ground-truth/1.2.276.0.28.3.0.14.4.0.20090213134050413.zraw
deleted file mode 100644
index 15708af..0000000
Binary files a/evalutils/templates/container/{{ cookiecutter.package_name }}/ground-truth/1.2.276.0.28.3.0.14.4.0.20090213134050413.zraw and /dev/null differ
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/ground-truth/detection-reference.csv b/evalutils/templates/container/{{ cookiecutter.package_name }}/ground-truth/detection-reference.csv
deleted file mode 100644
index 35c2c8b..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/ground-truth/detection-reference.csv
+++ /dev/null
@@ -1,8 +0,0 @@
-image_id, x, y, score
-case_001, 1, 1, 1
-case_001, 2, 1, 1
-case_001, 0, 0, 1
-case_002, 10, 10, 1
-case_002, 15, 15, 1
-case_003, 20, 20, 1
-case_004, 1, 2, 1
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/ground-truth/reference.csv b/evalutils/templates/container/{{ cookiecutter.package_name }}/ground-truth/reference.csv
deleted file mode 100644
index 11ffb78..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/ground-truth/reference.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-case, class
-1, 1
-2, 0
-3, 1
-4, 0
-5, 1
-6, 0
-7, 1
-8, 0
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/process.py.j2 b/evalutils/templates/container/{{ cookiecutter.package_name }}/process.py.j2
deleted file mode 100644
index c067b22..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/process.py.j2
+++ /dev/null
@@ -1,59 +0,0 @@
-{% if cookiecutter.task_kind == "Classification" %}
-from typing import Dict
-{% endif %}
-import SimpleITK
-import numpy as np
-{% if cookiecutter.task_kind == "Detection" %}
-from pandas import DataFrame
-from scipy.ndimage import center_of_mass, label
-{% endif %}
-from evalutils import {{ cookiecutter.task_kind }}Algorithm
-
-
-class {{ cookiecutter.package_name|capitalize }}({{ cookiecutter.task_kind }}Algorithm):
-{% if cookiecutter.task_kind == "Detection" %}
-    def predict(self, *, input_image: SimpleITK.Image) -> DataFrame:
-        # Extract a numpy array with image data from the SimpleITK Image
-        image_data = SimpleITK.GetArrayFromImage(input_image)
-
-        # Detection: Compute connected components of the maximum values
-        # in the input image and compute their center of mass
-        sample_mask = image_data >= np.max(image_data)
-        labels, num_labels = label(sample_mask)
-        candidates = center_of_mass(
-            input=sample_mask, labels=labels, index=np.arange(num_labels) + 1
-        )
-
-        # Scoring: Score each candidate cluster with the value at its center
-        candidate_scores = [
-            image_data[tuple(coord)]
-            for coord in np.array(candidates).astype(np.uint16)
-        ]
-
-        # Serialize candidates and scores as a list of dictionary entries
-        data = self._serialize_candidates(
-            candidates=candidates,
-            candidate_scores=candidate_scores,
-            ref_image=input_image,
-        )
-
-        # Convert serialized candidates to a pandas.DataFrame
-        return DataFrame(data)
-{% elif cookiecutter.task_kind == "Segmentation" %}
-    def predict(self, *, input_image: SimpleITK.Image) -> SimpleITK.Image:
-        # Segment all values greater than 2 in the input image
-        return SimpleITK.BinaryThreshold(
-            image1=input_image, lowerThreshold=2, insideValue=1, outsideValue=0
-        )
-{% elif cookiecutter.task_kind == "Classification" %}
-    def predict(self, *, input_image: SimpleITK.Image) -> Dict:
-        # Checks if there are any nodules voxels (> 1) in the input image
-        return dict(
-            values_exceeding_one=bool(
-                np.any(SimpleITK.GetArrayFromImage(input_image) > 1)
-            )
-        )
-{% endif %}
-
-if __name__ == "__main__":
-    {{ cookiecutter.package_name|capitalize }}().process()
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/requirements.in b/evalutils/templates/container/{{ cookiecutter.package_name }}/requirements.in
deleted file mode 100644
index 9d9b709..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/requirements.in
+++ /dev/null
@@ -1,3 +0,0 @@
-{%- for package, version in cookiecutter.requirements.items() %}
-{{ package }}{{ version }}
-{%- endfor %}
diff --git a/evalutils/utils.py b/evalutils/utils.py
deleted file mode 100644
index 313cfe6..0000000
--- a/evalutils/utils.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import shutil
-import subprocess
-import warnings
-from pathlib import Path
-
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore", category=UserWarning)
-    # Suppress "Setuptools is replacing distutils"
-    from piptools.scripts.compile import cli
-
-EOL_UNIX = b"\n"
-EOL_WIN = b"\r\n"
-EOL_MAC = b"\r"
-
-
-def generate_source_wheel(dest_dir: Path):
-    dist_dir = Path(__file__).parent.parent.absolute() / "dist"
-
-    subprocess.check_call(["poetry", "build"])
-
-    dest_dir.mkdir(exist_ok=True, parents=True)
-
-    for file in dist_dir.rglob("*.whl"):
-        shutil.copyfile(file, dest_dir / file.name)
-
-
-def convert_line_endings():
-    """Enforce unix line endings for the generated files"""
-    files = []
-    for ext in [
-        ".py",
-        ".sh",
-        "Dockerfile",
-        ".txt",
-        ".csv",
-        ".mhd",
-        ".gitignore",
-    ]:
-        files.extend(Path(".").glob(f"**/*{ext}"))
-
-    for file in files:
-        with open(str(file), "rb") as f:
-            lines = f.read()
-
-        lines = lines.replace(EOL_WIN, EOL_UNIX).replace(EOL_MAC, EOL_UNIX)
-
-        with open(str(file), "wb") as f:
-            f.write(lines)
-
-
-def generate_requirements_txt():
-    cli(["--resolver", "backtracking", "--quiet"])
diff --git a/evalutils/validators.py b/evalutils/validators.py
deleted file mode 100644
index a50d13f..0000000
--- a/evalutils/validators.py
+++ /dev/null
@@ -1,149 +0,0 @@
-from abc import ABC, abstractmethod
-from pathlib import Path
-from typing import Tuple
-
-from pandas import DataFrame
-
-from .exceptions import ValidationError
-from .io import first_int_in_filename_key
-
-
-class DataFrameValidator(ABC):
-    @abstractmethod
-    def validate(self, *, df: DataFrame):
-        """Validates a single aspect of a DataFrame
-
-        Parameters
-        ----------
-        df
-            The DataFrame to be validated
-
-        Returns
-        -------
-            None if the DataFrame is valid
-
-        Raises
-        ------
-        ValidationError
-            If the DataFrame is not valid
-
-        """
-        raise ValidationError
-
-
-class UniquePathIndicesValidator(DataFrameValidator):
-    """
-    Validates that the indicies from the filenames are unique
-    """
-
-    def validate(self, *, df: DataFrame):
-        try:
-            paths = df["path"]
-        except KeyError:
-            raise ValidationError("Column `path` not found in DataFrame.")
-
-        idx = [first_int_in_filename_key(Path(p)) for p in paths]
-
-        if len(set(idx)) != len(paths):
-            raise ValidationError(
-                "The first number is each filename is not unique, please "
-                "check that your files are named correctly."
-            )
-
-
-class UniqueImagesValidator(DataFrameValidator):
-    """
-    Validates that each image in the set is unique
-    """
-
-    def validate(self, *, df: DataFrame):
-        try:
-            hashes = df["hash"]
-        except KeyError:
-            raise ValidationError("Column `hash` not found in DataFrame.")
-
-        if len(set(hashes)) != len(hashes):
-            raise ValidationError(
-                "The images are not unique, please submit a unique image for "
-                "each case."
-            )
-
-
-class ExpectedColumnNamesValidator(DataFrameValidator):
-    def __init__(
-        self, *, expected: Tuple[str, ...], extra_cols_check: bool = True
-    ):
-        """
-        Validates that the DataFrame has the expected columns
-
-        Parameters
-        ----------
-        expected
-            The expected columns in the DataFrame
-        extra_cols_check
-            Perform the check for extra columns, default is true but you may
-            want to disable this if you're sure that extra columns can be
-            ignored.
-
-        Raises
-        ------
-        ValueError
-            If no columns are defined
-
-        """
-        if len(expected) == 0:
-            raise ValueError(
-                "You must define what columns you expect to find in the "
-                f"DataFrame in order to use {self.__class__.__name__}."
-            )
-
-        self._expected = expected
-        self._extra_cols_check = extra_cols_check
-        super().__init__()
-
-    def validate(self, *, df: DataFrame):
-        undefined_cols = [c for c in self._expected if c not in df.columns]
-
-        if undefined_cols:
-            raise ValidationError(
-                f"We expected to find the following columns but we didn't: "
-                f"{undefined_cols}. Please check the column labels, and "
-                f"note that this is case sensitive. We only found: "
-                f"{df.columns}."
-            )
-
-        extra_cols = [c for c in df.columns if c not in self._expected]
-
-        if self._extra_cols_check and extra_cols:
-            raise ValidationError(
-                f"We only expected to find the columns {self._expected}. "
-                f"However, we also found that extra columns were defined: "
-                f"{extra_cols}. Please remove them."
-            )
-
-
-class NumberOfCasesValidator(DataFrameValidator):
-    def __init__(self, *, num_cases: int):
-        """
-        Validates that there are the correct number of cases in the set.
-
-        Parameters
-        ----------
-        num_cases
-            The number of cases that we expect to find.
-        """
-        if num_cases <= 0:
-            raise ValueError(
-                "The expected number of cases must be greater than zero in "
-                f"{self.__class__.__name__}."
-            )
-
-        self._num_cases = num_cases
-        super().__init__()
-
-    def validate(self, *, df: DataFrame):
-        if len(df) != self._num_cases:
-            raise ValidationError(
-                f"We expected to find {self._num_cases}, but we found "
-                f"{len(df)}. Please correct the number of predictions."
-            )
diff --git a/pyproject.toml b/pyproject.toml
index fc5d5a5..0d82cff 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "evalutils"
-version = "0.4.2"
+version = "0.5.0"
 description = "evalutils helps users create extensions for grand-challenge.org"
 authors = ["James Meakin <evalutils@jmsmkn.com>"]
 license = "MIT"
@@ -17,19 +17,10 @@ classifiers = [
 evalutils = "evalutils.__main__:main"
 
 [tool.poetry.dependencies]
-python = "^3.8"
-imageio = { version = ">=2.31", extras=["tifffile"] }
-# Exclude 2.1.1.1 due to
-# https://github.com/SimpleITK/SimpleITK/issues/1627
-# and https://github.com/python-poetry/poetry/issues/2453
-SimpleITK = ">=2.0,!=2.1.1.1"
-cookiecutter = "*"
-click = "*"
+python = "^3.9"
 scipy = "*"
+numpy = "*"
 scikit-learn = "*"
-numpy = ">=1.22"
-pandas = ">=1.3"
-pip-tools = ">=6"
 
 [tool.poetry.dev-dependencies]
 pytest = "*"
@@ -38,6 +29,7 @@ pytest-randomly = "*"
 pytest-cov = "*"
 sphinx = "*"
 sphinx_autodoc_typehints = "*"
+SimpleITK = "*"
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]
@@ -50,7 +42,7 @@ line_length = 79
 
 [tool.black]
 line-length = 79
-target-version = ['py38']
+target-version = ['py39']
 
 [tool.pytest.ini_options]
 minversion = "6.0"
@@ -60,26 +52,20 @@ testpaths = [
 python_files = "tests.py test_*.py *_tests.py"
 addopts = "--strict-markers --showlocals -n auto --dist loadscope --durations=10"
 markers = [
-    "slow",
-]
-filterwarnings = [
-    "error",
-    "ignore:.*The default dtype for empty Series will be 'object' instead of 'float64' in a future version.*",
-    "ignore:.*Use pandas.concat instead\\.:FutureWarning",
 ]
 
 [tool.tox]
 legacy_tox_ini = """
 [tox]
 isolated_build = True
-envlist = py38, py39, py310, p311
+envlist = py39, py310, py311, py312
 
 [gh-actions]
 python =
-    3.8: py38
     3.9: py39
     3.10: py310
     3.11: py311
+    3.12: py312
 
 [testenv]
 allowlist_externals =
diff --git a/setup.cfg b/setup.cfg
index bc1957c..5fc3ebc 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -2,7 +2,7 @@
 collect_ignore = ['setup.py']
 
 [mypy]
-python_version = 3.11
+python_version = 3.12
 # disallow_untyped_defs = True
 
 [flake8]


Test patch:
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/algorithm_test/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.mhd b/evalutils/templates/container/{{ cookiecutter.package_name }}/algorithm_test/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.mhd
deleted file mode 100644
index 32192c6..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/algorithm_test/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.mhd
+++ /dev/null
@@ -1,14 +0,0 @@
-ObjectType = Image
-NDims = 3
-BinaryData = True
-BinaryDataByteOrderMSB = False
-CompressedData = True
-CompressedDataSize = 372164
-TransformMatrix = 1 0 0 0 1 0 0 0 0.99999999999999989
-Offset = -157.67773 -311.67773 -438.39999999999998
-CenterOfRotation = 0 0 0
-AnatomicalOrientation = RAI
-ElementSpacing = 0.64453125 0.64453125 1.7999999523162842
-DimSize = 512 512 194
-ElementType = MET_UCHAR
-ElementDataFile = 1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.zraw
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/algorithm_test/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.zraw b/evalutils/templates/container/{{ cookiecutter.package_name }}/algorithm_test/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.zraw
deleted file mode 100644
index acd479f..0000000
Binary files a/evalutils/templates/container/{{ cookiecutter.package_name }}/algorithm_test/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.zraw and /dev/null differ
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/algorithm_test/results_classification.json b/evalutils/templates/container/{{ cookiecutter.package_name }}/algorithm_test/results_classification.json
deleted file mode 100644
index b07e10e..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/algorithm_test/results_classification.json
+++ /dev/null
@@ -1,16 +0,0 @@
-[
-    {
-        "outputs": [
-            {
-                "values_exceeding_one": true
-            }
-        ],
-        "inputs": [
-            {
-                "type": "metaio_image",
-                "filename": "1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.mhd"
-            }
-        ],
-        "error_messages": []
-    }
-]
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/algorithm_test/results_detection.json b/evalutils/templates/container/{{ cookiecutter.package_name }}/algorithm_test/results_detection.json
deleted file mode 100644
index cec1f44..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/algorithm_test/results_detection.json
+++ /dev/null
@@ -1,54 +0,0 @@
-[
-    {
-        "outputs": [
-            {
-                "type": "candidates",
-                "data": {
-                    "coordX": {
-                        "0": 87.57278510989013,
-                        "1": -102.56700041330646,
-                        "2": -46.48898710227273,
-                        "3": -81.94195555672952,
-                        "4": 74.68088937924207,
-                        "5": -32.310027390109894,
-                        "6": -47.78179930672951
-                    },
-                    "coordY": {
-                        "0": -97.37179765109889,
-                        "1": -119.2894046471774,
-                        "2": -107.0399459090909,
-                        "3": -183.73199080949286,
-                        "4": -153.42708043127826,
-                        "5": -108.32882890109889,
-                        "6": -108.32183455949286
-                    },
-                    "coordZ": {
-                        "0": -359.8250570265801,
-                        "1": -315.09604432215787,
-                        "2": -298.91487972815173,
-                        "3": -228.51626043648344,
-                        "4": -198.08168057440633,
-                        "5": -152.8250625102075,
-                        "6": -133.11626296372037
-                    },
-                    "score": {
-                        "0": 2,
-                        "1": 2,
-                        "2": 2,
-                        "3": 2,
-                        "4": 2,
-                        "5": 2,
-                        "6": 2
-                    }
-                }
-            }
-        ],
-        "inputs": [
-            {
-                "type": "metaio_image",
-                "filename": "1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.mhd"
-            }
-        ],
-        "error_messages": []
-    }
-]
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/algorithm_test/results_segmentation.json b/evalutils/templates/container/{{ cookiecutter.package_name }}/algorithm_test/results_segmentation.json
deleted file mode 100644
index f97cfa1..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/algorithm_test/results_segmentation.json
+++ /dev/null
@@ -1,17 +0,0 @@
-[
-    {
-        "outputs": [
-            {
-                "type": "metaio_image",
-                "filename": "1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.mhd"
-            }
-        ],
-        "inputs": [
-            {
-                "type": "metaio_image",
-                "filename": "1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.mhd"
-            }
-        ],
-        "error_messages": []
-    }
-]
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/evaluation_test/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.mhd b/evalutils/templates/container/{{ cookiecutter.package_name }}/evaluation_test/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.mhd
deleted file mode 100644
index 310baac..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/evaluation_test/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.mhd
+++ /dev/null
@@ -1,14 +0,0 @@
-ObjectType = Image
-NDims = 3
-BinaryData = True
-BinaryDataByteOrderMSB = False
-CompressedData = True
-CompressedDataSize = 529641
-TransformMatrix = 1 0 0 0 1 0 0 0 1
-Offset = -161.5 -175 -276.875
-CenterOfRotation = 0 0 0
-AnatomicalOrientation = RAI
-ElementSpacing = 0.683594 0.683594 0.625
-DimSize = 512 512 476
-ElementType = MET_INT
-ElementDataFile = 1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.zraw
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/evaluation_test/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.zraw b/evalutils/templates/container/{{ cookiecutter.package_name }}/evaluation_test/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.zraw
deleted file mode 100644
index e45a114..0000000
Binary files a/evalutils/templates/container/{{ cookiecutter.package_name }}/evaluation_test/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.zraw and /dev/null differ
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/evaluation_test/1.2.276.0.28.3.0.14.4.0.20090213134050413.mhd b/evalutils/templates/container/{{ cookiecutter.package_name }}/evaluation_test/1.2.276.0.28.3.0.14.4.0.20090213134050413.mhd
deleted file mode 100644
index 0d5f19f..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/evaluation_test/1.2.276.0.28.3.0.14.4.0.20090213134050413.mhd
+++ /dev/null
@@ -1,14 +0,0 @@
-ObjectType = Image
-NDims = 3
-BinaryData = True
-BinaryDataByteOrderMSB = False
-CompressedData = True
-CompressedDataSize = 414098
-TransformMatrix = 1 0 0 0 1 0 0 0 1
-Offset = -163.672 -317.672 -237.9
-CenterOfRotation = 0 0 0
-AnatomicalOrientation = RAI
-ElementSpacing = 0.65625 0.65625 0.8
-DimSize = 512 512 381
-ElementType = MET_INT
-ElementDataFile = 1.2.276.0.28.3.0.14.4.0.20090213134050413.zraw
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/evaluation_test/1.2.276.0.28.3.0.14.4.0.20090213134050413.zraw b/evalutils/templates/container/{{ cookiecutter.package_name }}/evaluation_test/1.2.276.0.28.3.0.14.4.0.20090213134050413.zraw
deleted file mode 100644
index 4c6454f..0000000
Binary files a/evalutils/templates/container/{{ cookiecutter.package_name }}/evaluation_test/1.2.276.0.28.3.0.14.4.0.20090213134050413.zraw and /dev/null differ
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/evaluation_test/detection-submission.csv b/evalutils/templates/container/{{ cookiecutter.package_name }}/evaluation_test/detection-submission.csv
deleted file mode 100644
index aa1acd5..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/evaluation_test/detection-submission.csv
+++ /dev/null
@@ -1,17 +0,0 @@
-image_id, x, y, score
-case_001, 1, 1, 0.1
-case_001, 2, 2, 0.2
-case_001, 3, 3, 0.3
-case_001, 4, 4, 0.4
-case_001, 5, 5, 0.5
-case_001, 6, 6, 0.6
-case_001, 7, 7, 0.7
-case_002, 10, 10, 0.8
-case_002, 11, 11, 0.9
-case_002, 12, 12, 1.0
-case_002, 13, 13, 0.7
-case_002, 14, 14, 0.6
-case_002, 15, 15, 0.5
-case_003, 20, 20, 1.0
-case_003, 21, 21, 0.5
-case_005, 1, 2, 0.8
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/evaluation_test/submission.csv b/evalutils/templates/container/{{ cookiecutter.package_name }}/evaluation_test/submission.csv
deleted file mode 100644
index 633ac14..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/evaluation_test/submission.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-case, class
-1, 1
-2, 1
-3, 1
-4, 1
-5, 1
-6, 1
-7, 1
-8, 1
diff --git a/evalutils/templates/container/{{ cookiecutter.package_name }}/test.sh b/evalutils/templates/container/{{ cookiecutter.package_name }}/test.sh
deleted file mode 100755
index aa97400..0000000
--- a/evalutils/templates/container/{{ cookiecutter.package_name }}/test.sh
+++ /dev/null
@@ -1,51 +0,0 @@
-#!/usr/bin/env bash
-
-SCRIPTPATH="$( cd "$(dirname "$0")" ; pwd -P )"
-
-./build.sh
-
-VOLUME_SUFFIX=$(dd if=/dev/urandom bs=32 count=1 | md5sum | cut --delimiter=' ' --fields=1)
-{% if cookiecutter.template_kind == "Algorithm" -%}
-# Maximum is currently 30g, configurable in your algorithm image settings on grand challenge
-{%- endif %}
-MEM_LIMIT="4g"
-
-docker volume create {{ cookiecutter.package_name|lower }}-output-$VOLUME_SUFFIX
-
-# Do not change any of the parameters to docker run, these are fixed
-docker run --rm \
-        --memory="${MEM_LIMIT}" \
-        --memory-swap="${MEM_LIMIT}" \
-        --network="none" \
-        --cap-drop="ALL" \
-        --security-opt="no-new-privileges" \
-        --shm-size="128m" \
-        --pids-limit="256" \
-        -v $SCRIPTPATH/test/:/input/ \
-        -v {{ cookiecutter.package_name|lower }}-output-$VOLUME_SUFFIX:/output/ \
-        {{ cookiecutter.package_name|lower }}
-
-{% if cookiecutter.template_kind == "Evaluation" -%}
-docker run --rm \
-        -v {{ cookiecutter.package_name|lower }}-output-$VOLUME_SUFFIX:/output/ \
-        python:{{ cookiecutter.python_major_version }}.{{ cookiecutter.python_minor_version }}-slim cat /output/metrics.json | python -m json.tool
-{%- endif %}
-
-{% if cookiecutter.template_kind == "Algorithm" -%}
-docker run --rm \
-        -v {{ cookiecutter.package_name|lower }}-output-$VOLUME_SUFFIX:/output/ \
-        python:{{ cookiecutter.python_major_version }}.{{ cookiecutter.python_minor_version }}-slim cat /output/results.json | python -m json.tool
-
-docker run --rm \
-        -v {{ cookiecutter.package_name|lower }}-output-$VOLUME_SUFFIX:/output/ \
-        -v $SCRIPTPATH/test/:/input/ \
-        python:{{ cookiecutter.python_major_version }}.{{ cookiecutter.python_minor_version }}-slim python -c "import json, sys; f1 = json.load(open('/output/results.json')); f2 = json.load(open('/input/expected_output.json')); sys.exit(f1 != f2);"
-
-if [ $? -eq 0 ]; then
-    echo "Tests successfully passed..."
-else
-    echo "Expected output was not found..."
-fi
-{%- endif %}
-
-docker volume rm {{ cookiecutter.package_name|lower }}-output-$VOLUME_SUFFIX
diff --git a/tests/resources/classification/reference/reference.csv b/tests/resources/classification/reference/reference.csv
deleted file mode 100644
index 11ffb78..0000000
--- a/tests/resources/classification/reference/reference.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-case, class
-1, 1
-2, 0
-3, 1
-4, 0
-5, 1
-6, 0
-7, 1
-8, 0
diff --git a/tests/resources/classification/submission/submission.csv b/tests/resources/classification/submission/submission.csv
deleted file mode 100644
index 633ac14..0000000
--- a/tests/resources/classification/submission/submission.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-case, class
-1, 1
-2, 1
-3, 1
-4, 1
-5, 1
-6, 1
-7, 1
-8, 1
diff --git a/tests/resources/csv/algorithm_result.csv b/tests/resources/csv/algorithm_result.csv
deleted file mode 100644
index 472a5fe..0000000
--- a/tests/resources/csv/algorithm_result.csv
+++ /dev/null
@@ -1,13202 +0,0 @@
-file_id, label
-01_01_Normalized_1001_856,0
-01_01_Normalized_1005_120,0
-01_01_Normalized_1019_1202,0
-01_01_Normalized_1028_1337,0
-01_01_Normalized_1032_1577,0
-01_01_Normalized_103_1844,0
-01_01_Normalized_1048_1924,0
-01_01_Normalized_105_1009,0
-01_01_Normalized_105_143,0
-01_01_Normalized_1076_768,0
-01_01_Normalized_108_1291,0
-01_01_Normalized_1099_1096,0
-01_01_Normalized_1103_1773,0
-01_01_Normalized_1120_360,0
-01_01_Normalized_1121_1468,0
-01_01_Normalized_1134_657,0
-01_01_Normalized_1142_1691,0
-01_01_Normalized_1142_801,0
-01_01_Normalized_1157_1547,0
-01_01_Normalized_1165_1835,0
-01_01_Normalized_1194_391,0
-01_01_Normalized_1197_1816,0
-01_01_Normalized_119_1185,0
-01_01_Normalized_1203_1041,0
-01_01_Normalized_1210_302,0
-01_01_Normalized_1219_876,0
-01_01_Normalized_1255_1877,0
-01_01_Normalized_1264_1948,0
-01_01_Normalized_1268_789,0
-01_01_Normalized_1273_1608,0
-01_01_Normalized_1285_1065,0
-01_01_Normalized_1286_1110,0
-01_01_Normalized_1300_129,0
-01_01_Normalized_1303_1038,0
-01_01_Normalized_1305_1883,0
-01_01_Normalized_1333_903,0
-01_01_Normalized_134_1350,0
-01_01_Normalized_1365_448,0
-01_01_Normalized_1368_822,0
-01_01_Normalized_1374_1261,0
-01_01_Normalized_1387_284,0
-01_01_Normalized_1397_613,0
-01_01_Normalized_139_484,0
-01_01_Normalized_140_1774,0
-01_01_Normalized_1448_990,0
-01_01_Normalized_1453_1340,0
-01_01_Normalized_1456_624,0
-01_01_Normalized_1457_1789,0
-01_01_Normalized_1460_673,0
-01_01_Normalized_1512_555,0
-01_01_Normalized_1518_109,0
-01_01_Normalized_1541_1233,0
-01_01_Normalized_1566_1804,0
-01_01_Normalized_1568_1285,0
-01_01_Normalized_1582_774,0
-01_01_Normalized_1588_1456,0
-01_01_Normalized_1588_563,0
-01_01_Normalized_1594_1598,0
-01_01_Normalized_1603_1524,0
-01_01_Normalized_1613_212,0
-01_01_Normalized_1632_1609,0
-01_01_Normalized_1646_1021,0
-01_01_Normalized_1649_876,0
-01_01_Normalized_1679_1006,0
-01_01_Normalized_1681_1375,0
-01_01_Normalized_1710_1384,0
-01_01_Normalized_1727_1697,0
-01_01_Normalized_1748_1475,0
-01_01_Normalized_1766_232,0
-01_01_Normalized_1769_1055,1
-01_01_Normalized_1771_652,0
-01_01_Normalized_1773_698,0
-01_01_Normalized_1781_572,0
-01_01_Normalized_1804_1907,0
-01_01_Normalized_1807_1690,0
-01_01_Normalized_1814_548,0
-01_01_Normalized_1817_1157,0
-01_01_Normalized_1826_1622,0
-01_01_Normalized_1834_251,0
-01_01_Normalized_1836_1376,0
-01_01_Normalized_1839_1103,0
-01_01_Normalized_1848_1257,0
-01_01_Normalized_1853_1872,0
-01_01_Normalized_1862_1361,1
-01_01_Normalized_189_1128,0
-01_01_Normalized_189_1452,0
-01_01_Normalized_1901_799,0
-01_01_Normalized_190_645,0
-01_01_Normalized_1933_1873,0
-01_01_Normalized_1936_1203,0
-01_01_Normalized_1939_106,0
-01_01_Normalized_1946_1002,0
-01_01_Normalized_194_1228,0
-01_01_Normalized_225_1829,0
-01_01_Normalized_232_1746,0
-01_01_Normalized_233_658,0
-01_01_Normalized_265_424,0
-01_01_Normalized_270_1775,1
-01_01_Normalized_299_1906,0
-01_01_Normalized_306_1542,0
-01_01_Normalized_327_133,0
-01_01_Normalized_327_677,0
-01_01_Normalized_333_378,0
-01_01_Normalized_361_1144,0
-01_01_Normalized_364_622,0
-01_01_Normalized_371_1302,0
-01_01_Normalized_373_1675,0
-01_01_Normalized_385_68,0
-01_01_Normalized_396_1628,0
-01_01_Normalized_397_229,0
-01_01_Normalized_402_1519,0
-01_01_Normalized_402_1947,0
-01_01_Normalized_431_988,0
-01_01_Normalized_438_1224,0
-01_01_Normalized_444_1477,0
-01_01_Normalized_446_684,0
-01_01_Normalized_448_1253,0
-01_01_Normalized_457_491,0
-01_01_Normalized_462_1067,0
-01_01_Normalized_465_126,0
-01_01_Normalized_479_1146,0
-01_01_Normalized_492_623,0
-01_01_Normalized_495_764,0
-01_01_Normalized_502_361,0
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diff --git a/tests/resources/detection/reference/reference.csv b/tests/resources/detection/reference/reference.csv
deleted file mode 100644
index e152832..0000000
--- a/tests/resources/detection/reference/reference.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-image_id, x, y, score
-a, 1, 1, 1
-a, 2, 1, 1
-a, 0, 0, 1
-b, 10, 10, 1
-b, 15, 15, 1
-c, 20, 20, 1
diff --git a/tests/resources/detection/submission/submission.csv b/tests/resources/detection/submission/submission.csv
deleted file mode 100644
index f127da8..0000000
--- a/tests/resources/detection/submission/submission.csv
+++ /dev/null
@@ -1,16 +0,0 @@
-image_id, x, y, score
-a, 1, 1, 0.1
-a, 2, 2, 0.2
-a, 3, 3, 0.3
-a, 4, 4, 0.4
-a, 5, 5, 0.5
-a, 6, 6, 0.6
-a, 7, 7, 0.7
-b, 10, 10, 0.8
-b, 11, 11, 0.9
-b, 12, 12, 1.0
-b, 13, 13, 0.7
-b, 14, 14, 0.6
-b, 15, 15, 0.5
-c, 20, 20, 1.0
-c, 21, 21, 0.5
diff --git a/tests/resources/images/1_mask.png b/tests/resources/images/1_mask.png
deleted file mode 100644
index 7306972..0000000
Binary files a/tests/resources/images/1_mask.png and /dev/null differ
diff --git a/tests/resources/images/2_mask.png b/tests/resources/images/2_mask.png
deleted file mode 100644
index 3b867b6..0000000
Binary files a/tests/resources/images/2_mask.png and /dev/null differ
diff --git a/tests/resources/itk/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.mhd b/tests/resources/itk/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.mhd
deleted file mode 100644
index 310baac..0000000
--- a/tests/resources/itk/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.mhd
+++ /dev/null
@@ -1,14 +0,0 @@
-ObjectType = Image
-NDims = 3
-BinaryData = True
-BinaryDataByteOrderMSB = False
-CompressedData = True
-CompressedDataSize = 529641
-TransformMatrix = 1 0 0 0 1 0 0 0 1
-Offset = -161.5 -175 -276.875
-CenterOfRotation = 0 0 0
-AnatomicalOrientation = RAI
-ElementSpacing = 0.683594 0.683594 0.625
-DimSize = 512 512 476
-ElementType = MET_INT
-ElementDataFile = 1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.zraw
diff --git a/tests/resources/itk/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.zraw b/tests/resources/itk/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.zraw
deleted file mode 100644
index e45a114..0000000
Binary files a/tests/resources/itk/1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.zraw and /dev/null differ
diff --git a/tests/resources/json/results_2d.json b/tests/resources/json/results_2d.json
deleted file mode 100644
index 4fd9114..0000000
--- a/tests/resources/json/results_2d.json
+++ /dev/null
@@ -1,16 +0,0 @@
-[
-    {
-        "outputs": [
-            {
-                "type": "candidates",
-                "data": {
-                    "coordX": {"0": 85.5, "1": 172.5},
-                    "coordY": {"0": 298.5, "1": 317.5},
-                    "score": {"0": 2, "1": 2}
-                }
-            }
-        ],
-        "inputs": [{"type": "metaio_image", "filename": "2dtest.mha"}],
-        "error_messages": []
-    }
-]
diff --git a/tests/resources/json/results_empty.json b/tests/resources/json/results_empty.json
deleted file mode 100644
index 7f7dde5..0000000
--- a/tests/resources/json/results_empty.json
+++ /dev/null
@@ -1,16 +0,0 @@
-[
-    {
-        "outputs": [
-            {
-                "type": "candidates",
-                "data": {
-                    "coordX": {"0": 49.5},
-                    "coordY": {"0": 49.5},
-                    "score": {"0": 0}
-                }
-            }
-        ],
-        "inputs": [{"type": "metaio_image", "filename": "emptytest.mha"}],
-        "error_messages": []
-    }
-]
diff --git a/tests/test_abbreviatedchoice.py b/tests/test_abbreviatedchoice.py
deleted file mode 100644
index 21a582c..0000000
--- a/tests/test_abbreviatedchoice.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import pytest
-
-from evalutils.cli import AbbreviatedChoice
-
-
-def test_abbreviated_choice_not_unique():
-    with pytest.raises(ValueError):
-        AbbreviatedChoice(choices=["Same", "same", "other"])
-
-
-@pytest.mark.parametrize(
-    "value, expected",
-    (
-        ("s", "same"),
-        ("S", "same"),
-        ("Same", "same"),
-        ("same", "same"),
-        ("other", "oTHER"),
-        ("O", "oTHER"),
-        ("o", "oTHER"),
-        ("OtHER", "oTHER"),
-    ),
-)
-def test_abbreviated_choice_convert(value: str, expected: str):
-    ac = AbbreviatedChoice(choices=["same", "oTHER"])
-    assert ac.convert(value=value, param=None, ctx=None) == expected
diff --git a/tests/test_algorithm.py b/tests/test_algorithm.py
deleted file mode 100644
index ca5ec40..0000000
--- a/tests/test_algorithm.py
+++ /dev/null
@@ -1,182 +0,0 @@
-import json
-import os
-import shutil
-from pathlib import Path
-from typing import Dict
-
-import numpy as np
-import SimpleITK
-from pandas import DataFrame
-from scipy.ndimage import center_of_mass, label
-
-from evalutils import (
-    ClassificationAlgorithm,
-    DetectionAlgorithm,
-    SegmentationAlgorithm,
-)
-
-TEMPLATE_TEST_DIR = (
-    Path(__file__).parent.parent
-    / "evalutils"
-    / "templates"
-    / "container"
-    / "{{ cookiecutter.package_name }}"
-    / "algorithm_test"
-)
-
-
-class DetectionAlgorithmTest(DetectionAlgorithm):
-    def predict(self, *, input_image: SimpleITK.Image) -> DataFrame:
-        # Extract a numpy array with image data from the SimpleITK Image
-        image_data = SimpleITK.GetArrayFromImage(input_image)
-
-        # Detection: Compute connected components of the maximum values
-        # in the input image and compute their center of mass
-        sample_mask = image_data == np.max(image_data)
-        labels, num_labels = label(sample_mask)
-        candidates = center_of_mass(
-            input=sample_mask, labels=labels, index=np.arange(num_labels) + 1
-        )
-
-        # Scoring: Score each candidate cluster with the value at its center
-        candidate_scores = [
-            image_data[tuple(coord)]
-            for coord in np.array(candidates).astype(np.uint16)
-        ]
-
-        # Serialize candidates and scores as a list of dictionary entries
-        data = self._serialize_candidates(
-            candidates=candidates,
-            candidate_scores=candidate_scores,
-            ref_image=input_image,
-        )
-
-        # Convert serialized candidates to a pandas.DataFrame
-        return DataFrame(data)
-
-
-class SegmentationAlgorithmTest(SegmentationAlgorithm):
-    def predict(self, *, input_image: SimpleITK.Image) -> SimpleITK.Image:
-        # Segment all values greater than 2 in the input image
-        return SimpleITK.BinaryThreshold(
-            image1=input_image, lowerThreshold=2, insideValue=1, outsideValue=0
-        )
-
-
-class ClassificationAlgorithmTest(ClassificationAlgorithm):
-    def predict(self, *, input_image: SimpleITK.Image) -> Dict:
-        # Checks if there are any nodules voxels (> 1) in the input image
-        return dict(
-            values_exceeding_one=bool(
-                np.any(SimpleITK.GetArrayFromImage(input_image) > 1)
-            )
-        )
-
-
-def test_classification_algorithm(tmpdir):
-    indir = Path(tmpdir / "input")
-    shutil.copytree(TEMPLATE_TEST_DIR, indir)
-    validate_algorithm_output(
-        input_dir=indir,
-        expected_results_file="results_classification.json",
-        algorithm_test_class=ClassificationAlgorithmTest,
-    )
-
-
-def test_segmentation_algorithm(tmpdir):
-    indir = Path(tmpdir / "input")
-    out_file = Path(
-        tmpdir
-        / "output"
-        / "images"
-        / "1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.mhd"
-    )
-    shutil.copytree(TEMPLATE_TEST_DIR, indir)
-    validate_algorithm_output(
-        input_dir=indir,
-        expected_results_file="results_segmentation.json",
-        algorithm_test_class=SegmentationAlgorithmTest,
-    )
-    assert out_file.exists()
-    out_img = SimpleITK.GetArrayFromImage(SimpleITK.ReadImage(str(out_file)))
-    in_img = SimpleITK.GetArrayFromImage(
-        SimpleITK.ReadImage(str(indir / out_file.name))
-    )
-    assert np.array_equal((in_img >= 2), (out_img > 0))
-
-
-def test_detection_algorithm(tmpdir):
-    indir = tmpdir / "input"
-    shutil.copytree(TEMPLATE_TEST_DIR, indir)
-    validate_algorithm_output(
-        input_dir=indir,
-        expected_results_file="results_detection.json",
-        algorithm_test_class=DetectionAlgorithmTest,
-    )
-
-
-def test_detection_algorithm_2d_input(tmpdir):
-    indir = tmpdir / "input"
-
-    os.makedirs(indir)
-    test_image = (
-        TEMPLATE_TEST_DIR
-        / "1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.mhd"
-    )
-    image_data = SimpleITK.GetArrayFromImage(
-        SimpleITK.ReadImage(str(test_image))
-    )[74, :, :]
-    SimpleITK.WriteImage(
-        SimpleITK.GetImageFromArray(image_data),
-        str(indir / "2dtest.mha"),
-        True,
-    )
-
-    validate_algorithm_output(
-        input_dir=indir,
-        expected_results_file="results_2d.json",
-        algorithm_test_class=DetectionAlgorithmTest,
-    )
-
-
-def test_detection_algorithm_empty_input(tmpdir):
-    indir = tmpdir / "input"
-
-    os.makedirs(indir)
-    SimpleITK.WriteImage(
-        SimpleITK.GetImageFromArray(np.zeros((100, 100), dtype=np.uint8)),
-        str(indir / "emptytest.mha"),
-        True,
-    )
-
-    validate_algorithm_output(
-        input_dir=indir,
-        expected_results_file="results_empty.json",
-        algorithm_test_class=DetectionAlgorithmTest,
-    )
-
-
-def validate_algorithm_output(
-    input_dir: Path, expected_results_file: str, algorithm_test_class: type
-):
-    output_dir = Path(input_dir).parent / "output"
-    output_dir.mkdir()
-    proc = algorithm_test_class()
-    proc._input_path = Path(input_dir)
-    proc._output_file = Path(output_dir) / "results.json"
-    proc._output_path = Path(output_dir) / "images"
-    proc.process()
-    results_file = output_dir / "results.json"
-    assert results_file.exists()
-    with open(str(results_file)) as f:
-        results = json.load(f)
-
-    expected_path = (
-        Path(__file__).parent / "resources" / "json" / expected_results_file
-    )
-    if not expected_path.exists():
-        expected_path = TEMPLATE_TEST_DIR / expected_results_file
-
-    with open(str(expected_path)) as f:
-        expected_result = json.load(f)
-    assert results == expected_result
diff --git a/tests/test_classification_evaluation.py b/tests/test_classification_evaluation.py
deleted file mode 100644
index c94e4e2..0000000
--- a/tests/test_classification_evaluation.py
+++ /dev/null
@@ -1,90 +0,0 @@
-from pathlib import Path
-
-import pytest
-from pandas import Series
-
-from evalutils import ClassificationEvaluation
-from evalutils.exceptions import ConfigurationError, FileLoaderError
-from evalutils.io import CSVLoader
-from evalutils.validators import ExpectedColumnNamesValidator
-
-
-class ClassificationTestEval(ClassificationEvaluation):
-    def __init__(self, outdir):
-        super().__init__(
-            file_loader=CSVLoader(),
-            ground_truth_path=(
-                Path(__file__).parent
-                / "resources"
-                / "classification"
-                / "reference"
-            ),
-            predictions_path=(
-                Path(__file__).parent
-                / "resources"
-                / "classification"
-                / "submission"
-            ),
-            output_file=Path(outdir) / "metrics.json",
-            join_key="case",
-            validators=(
-                ExpectedColumnNamesValidator(expected=("case", "class")),
-            ),
-        )
-
-
-def test_class_creation(tmpdir):
-    ClassificationTestEval(outdir=tmpdir).evaluate()
-
-
-def test_csv_with_no_join():
-    class C(ClassificationEvaluation):
-        def __init__(self, **kwargs):
-            super().__init__(
-                ground_truth_path=Path("/tmp"),
-                validators=(),
-                file_loader=CSVLoader(),
-                **kwargs,
-            )
-
-    with pytest.raises(ConfigurationError):
-        C()
-
-    # Check that it's ok when we define it
-    C(join_key="test")
-
-
-def test_wrong_loader():
-    class C(ClassificationEvaluation):
-        def __init__(self):
-            super().__init__(
-                file_loader=CSVLoader(),
-                ground_truth_path=(
-                    Path(__file__).parent / "resources" / "itk"
-                ),
-                join_key="test",
-                validators=(),
-            )
-
-    with pytest.raises(FileLoaderError):
-        C().evaluate()
-
-
-def test_series_aggregation(tmpdir):
-    class C(ClassificationTestEval):
-        def score_case(self, *, idx: int, case: Series):
-            return {
-                "accuracy": 1.0
-                if case["class_ground_truth"] == case["class_prediction"]
-                else 0.0,
-                "case_id": str(idx),
-            }
-
-    e = C(outdir=tmpdir)
-    e.evaluate()
-
-    assert e._metrics["aggregates"]["accuracy"]["mean"] == 0.5
-    assert len(e._metrics) == 2
-    assert len(e._metrics["case"]["accuracy"]) == 8
-    assert len(e._metrics["aggregates"]) == 2
-    assert len(e._metrics["aggregates"]["accuracy"]) == 8
diff --git a/tests/test_detection_evaluation.py b/tests/test_detection_evaluation.py
deleted file mode 100644
index 3eaa138..0000000
--- a/tests/test_detection_evaluation.py
+++ /dev/null
@@ -1,52 +0,0 @@
-from pathlib import Path
-
-from evalutils import DetectionEvaluation
-from evalutils.io import CSVLoader
-from evalutils.validators import ExpectedColumnNamesValidator
-
-
-class DetectionEvaluationTest(DetectionEvaluation):
-    def __init__(self, outdir):
-        super().__init__(
-            file_loader=CSVLoader(),
-            validators=(
-                ExpectedColumnNamesValidator(
-                    expected=("image_id", "x", "y", "score")
-                ),
-            ),
-            join_key="image_id",
-            ground_truth_path=(
-                Path(__file__).parent / "resources" / "detection" / "reference"
-            ),
-            predictions_path=(
-                Path(__file__).parent
-                / "resources"
-                / "detection"
-                / "submission"
-            ),
-            output_file=Path(outdir) / "metrics.json",
-            detection_threshold=0.5,
-            detection_radius=1.0,
-        )
-
-    def get_points(self, *, case, key: str):
-        try:
-            points = case.loc[key]
-        except KeyError:
-            return []
-
-        return [
-            (p["x"], p["y"])
-            for _, p in points.iterrows()
-            if p["score"] > self._detection_threshold
-        ]
-
-
-def test_detection_evaluation(tmpdir):
-    ev = DetectionEvaluationTest(outdir=tmpdir)
-    ev.evaluate()
-
-    assert len(ev._cases) == 21
-    assert ev._metrics["aggregates"]["precision"] == 0.25
-    assert ev._metrics["aggregates"]["recall"] == 1 / 3
-    assert ev._metrics["aggregates"]["f1_score"] == 4 / 14
diff --git a/tests/test_io.py b/tests/test_io.py
deleted file mode 100644
index 1108fe6..0000000
--- a/tests/test_io.py
+++ /dev/null
@@ -1,89 +0,0 @@
-from pathlib import Path
-
-import pytest
-from SimpleITK import GetArrayFromImage
-
-from evalutils.exceptions import FileLoaderError
-from evalutils.io import (
-    CSVLoader,
-    ImageIOLoader,
-    SimpleITKLoader,
-    first_int_in_filename_key,
-    get_first_int_in,
-)
-
-
-@pytest.mark.parametrize(
-    "filename,expected",
-    [
-        ("1.csv", 1),
-        ("02.csv", 2),
-        ("03_dfsdfs.csv", 3),
-        ("04_04_fdsa.csv", 4),
-        ("00005.00004.0003.fgdgdf.432.csv", 5000040003),
-        ("1 copy (1).csv", 1),
-    ],
-)
-def test_get_first_int_in(filename, expected):
-    assert get_first_int_in(filename) == expected
-
-
-def test_first_int_in_filename_key():
-    fname = Path(__file__).parent / "22" / "1_mask.png"
-    assert first_int_in_filename_key(fname) == f"{1:>64}"
-    fname = Path(__file__).parent / "22" / "nonumberhere.png"
-    assert first_int_in_filename_key(fname) == "nonumberhere"
-
-
-def test_image_io_loader():
-    fname = Path(__file__).parent / "resources" / "images" / "1_mask.png"
-    loader = ImageIOLoader()
-    o = loader.load(fname=fname)[0]
-    img = loader.load_image(fname)
-    assert o["path"] == fname
-    assert o["hash"] == loader.hash_image(img)
-    assert img.shape == (584, 565)
-
-    non_image_fname = (
-        Path(__file__).parent / "resources" / "csv" / "algorithm_result.csv"
-    )
-
-    with pytest.raises(FileLoaderError):
-        ImageIOLoader().load(fname=non_image_fname)
-
-
-def test_csv_loader():
-    fname = (
-        Path(__file__).parent / "resources" / "csv" / "algorithm_result.csv"
-    )
-    records = CSVLoader().load(fname=fname)
-
-    for record in records:
-        assert set(record.keys()) == {"file_id", "label"}
-
-    assert len(records) == 13201
-    non_csv_filename = (
-        Path(__file__).parent / "resources" / "images" / "1_mask.png"
-    )
-
-    with pytest.raises(FileLoaderError):
-        CSVLoader().load(fname=non_csv_filename)
-
-
-def test_itk_loader():
-    fname = (
-        Path(__file__).parent
-        / "resources"
-        / "itk"
-        / "1.0.000.000000.0.00.0.0000000000.0000.0000000000.000.mhd"
-    )
-    loader = SimpleITKLoader()
-    o = loader.load(fname=fname)[0]
-    img = loader.load_image(fname)
-
-    assert o["path"] == fname
-    assert o["hash"] == loader.hash_image(img)
-    assert GetArrayFromImage(img).shape == (476, 512, 512)
-
-    with pytest.raises(FileLoaderError):
-        SimpleITKLoader().load(fname=fname.with_name(f"{fname.stem}.zraw"))
diff --git a/tests/test_stats.py b/tests/test_stats.py
index 2534b66..a268227 100644
--- a/tests/test_stats.py
+++ b/tests/test_stats.py
@@ -1,11 +1,7 @@
-from typing import Tuple
-
 import numpy as np
 import pytest
-import scipy.ndimage as ndimage
 import SimpleITK
 import sklearn
-from numpy.testing import assert_array_almost_equal
 from scipy.ndimage import generate_binary_structure
 
 import evalutils.stats as stats
@@ -20,13 +16,11 @@ def reset_seeds():
 @pytest.mark.parametrize(
     "shape, dtype", (((12,), np.int32), ((10, 2), np.int32), ((10,), np.int8))
 )
-def test_edt32_indices_wrong_format(shape: Tuple[int, ...], dtype: np.dtype):
+def test_edt32_indices_wrong_format(shape: tuple[int, ...], dtype: np.dtype):
     x = np.random.random((10,)) > 0.5
     indices = np.zeros((x.ndim,) + shape, dtype=dtype)
     with pytest.raises(RuntimeError):
-        stats.distance_transform_edt_float32(
-            input=x, sampling=[1.2], indices=indices
-        )
+        stats.distance_transform_edt(input=x, sampling=[1.2], indices=indices)
 
 
 @pytest.mark.parametrize(
@@ -433,188 +427,3 @@ def test_cm_functions(a, b, classes):
     assert not np.allclose(r3, r4)
     assert not np.allclose(r1, r5)
     assert not np.allclose(r3, r5)
-
-
-# The following code is modified from the SciPy test suite
-#
-# Copyright (C) 2003-2005 Peter J. Verveer
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions
-# are met:
-#
-# 1. Redistributions of source code must retain the above copyright
-#    notice, this list of conditions and the following disclaimer.
-#
-# 2. Redistributions in binary form must reproduce the above
-#    copyright notice, this list of conditions and the following
-#    disclaimer in the documentation and/or other materials provided
-#    with the distribution.
-#
-# 3. The name of the author may not be used to endorse or promote
-#    products derived from this software without specific prior
-#    written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
-# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
-# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
-# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
-# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
-# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-INTEGER_TYPES = [
-    np.int8,
-    np.uint8,
-    np.int16,
-    np.uint16,
-    np.int32,
-    np.uint32,
-    np.int64,
-    np.uint64,
-]
-
-FLOAT_TYPES = [np.float32, np.float64]
-
-TYPES = INTEGER_TYPES + FLOAT_TYPES
-
-
-@pytest.mark.parametrize("type_", TYPES)
-def test_distance_transform_edt01(type_):
-    # euclidean distance transform (edt)
-    data = np.array(
-        [
-            [0, 0, 0, 0, 0, 0, 0, 0, 0],
-            [0, 0, 0, 0, 0, 0, 0, 0, 0],
-            [0, 0, 0, 1, 1, 1, 0, 0, 0],
-            [0, 0, 1, 1, 1, 1, 1, 0, 0],
-            [0, 0, 1, 1, 1, 1, 1, 0, 0],
-            [0, 0, 1, 1, 1, 1, 1, 0, 0],
-            [0, 0, 0, 1, 1, 1, 0, 0, 0],
-            [0, 0, 0, 0, 0, 0, 0, 0, 0],
-            [0, 0, 0, 0, 0, 0, 0, 0, 0],
-        ],
-        type_,
-    )
-    out, ft = stats.distance_transform_edt_float32(data, return_indices=True)
-    bf = ndimage.distance_transform_bf(data, "euclidean")
-    assert_array_almost_equal(bf, out)
-
-    dt = ft - np.indices(ft.shape[1:], dtype=ft.dtype)
-    dt = dt.astype(np.float64)
-    np.multiply(dt, dt, dt)
-    dt = np.add.reduce(dt, axis=0)
-    np.sqrt(dt, dt)
-
-    assert_array_almost_equal(bf, dt)
-
-
-@pytest.mark.parametrize("type_", TYPES)
-def test_distance_transform_edt02(type_):
-    data = np.array(
-        [
-            [0, 0, 0, 0, 0, 0, 0, 0, 0],
-            [0, 0, 0, 0, 0, 0, 0, 0, 0],
-            [0, 0, 0, 1, 1, 1, 0, 0, 0],
-            [0, 0, 1, 1, 1, 1, 1, 0, 0],
-            [0, 0, 1, 1, 1, 1, 1, 0, 0],
-            [0, 0, 1, 1, 1, 1, 1, 0, 0],
-            [0, 0, 0, 1, 1, 1, 0, 0, 0],
-            [0, 0, 0, 0, 0, 0, 0, 0, 0],
-            [0, 0, 0, 0, 0, 0, 0, 0, 0],
-        ],
-        type_,
-    )
-    tdt, tft = stats.distance_transform_edt_float32(data, return_indices=True)
-    dts = []
-    fts = []
-    dt = np.zeros(data.shape, dtype=np.float32)
-    stats.distance_transform_edt_float32(data, distances=dt)
-    dts.append(dt)
-    ft = stats.distance_transform_edt_float32(
-        data, return_distances=0, return_indices=True
-    )
-    fts.append(ft)
-    ft = np.indices(data.shape, dtype=np.int32)
-    stats.distance_transform_edt_float32(
-        data, return_distances=False, return_indices=True, indices=ft
-    )
-    fts.append(ft)
-    dt, ft = stats.distance_transform_edt_float32(data, return_indices=True)
-    dts.append(dt)
-    fts.append(ft)
-    dt = np.zeros(data.shape, dtype=np.float32)
-    ft = stats.distance_transform_edt_float32(
-        data, distances=dt, return_indices=True
-    )
-    dts.append(dt)
-    fts.append(ft)
-    ft = np.indices(data.shape, dtype=np.int32)
-    dt = stats.distance_transform_edt_float32(
-        data, return_indices=True, indices=ft
-    )
-    dts.append(dt)
-    fts.append(ft)
-    dt = np.zeros(data.shape, dtype=np.float32)
-    ft = np.indices(data.shape, dtype=np.int32)
-    stats.distance_transform_edt_float32(
-        data, distances=dt, return_indices=True, indices=ft
-    )
-    dts.append(dt)
-    fts.append(ft)
-    for dt in dts:
-        assert_array_almost_equal(tdt, dt)
-    for ft in fts:
-        assert_array_almost_equal(tft, ft)
-
-
-@pytest.mark.parametrize("type_", TYPES)
-def test_distance_transform_edt03(type_):
-    data = np.array(
-        [
-            [0, 0, 0, 0, 0, 0, 0, 0, 0],
-            [0, 0, 0, 0, 0, 0, 0, 0, 0],
-            [0, 0, 0, 1, 1, 1, 0, 0, 0],
-            [0, 0, 1, 1, 1, 1, 1, 0, 0],
-            [0, 0, 1, 1, 1, 1, 1, 0, 0],
-            [0, 0, 1, 1, 1, 1, 1, 0, 0],
-            [0, 0, 0, 1, 1, 1, 0, 0, 0],
-            [0, 0, 0, 0, 0, 0, 0, 0, 0],
-            [0, 0, 0, 0, 0, 0, 0, 0, 0],
-        ],
-        type_,
-    )
-    ref = ndimage.distance_transform_bf(data, "euclidean", sampling=[2, 2])
-    out = stats.distance_transform_edt_float32(data, sampling=[2, 2])
-    assert_array_almost_equal(ref, out)
-
-
-@pytest.mark.parametrize("type_", TYPES)
-def test_distance_transform_edt4(type_):
-    data = np.array(
-        [
-            [0, 0, 0, 0, 0, 0, 0, 0, 0],
-            [0, 0, 0, 0, 0, 0, 0, 0, 0],
-            [0, 0, 0, 1, 1, 1, 0, 0, 0],
-            [0, 0, 1, 1, 1, 1, 1, 0, 0],
-            [0, 0, 1, 1, 1, 1, 1, 0, 0],
-            [0, 0, 1, 1, 1, 1, 1, 0, 0],
-            [0, 0, 0, 1, 1, 1, 0, 0, 0],
-            [0, 0, 0, 0, 0, 0, 0, 0, 0],
-            [0, 0, 0, 0, 0, 0, 0, 0, 0],
-        ],
-        type_,
-    )
-    ref = ndimage.distance_transform_bf(data, "euclidean", sampling=[2, 1])
-    out = stats.distance_transform_edt_float32(data, sampling=[2, 1])
-    assert_array_almost_equal(ref, out)
-
-
-def test_distance_transform_edt5():
-    # Ticket #954 regression test
-    out = stats.distance_transform_edt_float32(False)
-    assert_array_almost_equal(out, [0.0])
diff --git a/tests/test_templates.py b/tests/test_templates.py
deleted file mode 100644
index 17a045d..0000000
--- a/tests/test_templates.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import json
-import os
-import subprocess
-from pathlib import Path
-
-import pytest
-
-
-def check_dict(check, expected):
-    """Recursively check a dictionary of dictionaries"""
-    for key, val in expected.items():
-        if isinstance(val, dict):
-            check_dict(check[key], val)
-        else:
-            assert check[key] == val
-
-
-@pytest.mark.slow
-@pytest.mark.parametrize(
-    ("kind", "expected"),
-    [
-        ("Classification", {"aggregates": {"accuracy_score": 0.5}}),
-        (
-            "Segmentation",
-            {"aggregates": {"DiceCoefficient": {"mean": 0.9557903761508626}}},
-        ),
-        (
-            "Detection",
-            {
-                "aggregates": {
-                    "false_negatives": {"sum": 5},
-                    "false_positives": {"sum": 7},
-                    "true_positives": {"sum": 2},
-                    "image_id": {"count": 5},
-                    "precision": 2 / 9,
-                    "recall": 2 / 7,
-                    "f1_score": (2 * 2) / ((2 * 2) + 5 + 7),
-                }
-            },
-        ),
-    ],
-)
-def test_evaluation_cli(tmpdir, kind, expected):
-    project_name = f"testeval{kind}"
-
-    files = os.listdir(tmpdir)
-    assert len(files) == 0
-
-    out = subprocess.check_output(
-        [
-            "evalutils",
-            "init",
-            "evaluation",
-            project_name,
-            f"--kind={kind}",
-            "--dev",
-        ],
-        cwd=tmpdir,
-    )
-
-    files = os.listdir(tmpdir)
-    assert project_name in files
-    assert f"Created project {project_name}" in out.decode()
-
-    project_dir = Path(tmpdir) / project_name
-
-    out = subprocess.check_output([str(project_dir / "build.sh")])
-
-    assert "Successfully built" in out.decode()
-    assert f"Successfully tagged {project_name.lower()}:latest" in out.decode()
-
-    out = subprocess.check_output([str(project_dir / "test.sh")])
-
-    # Grab the results json
-    out = out.decode().splitlines()
-    start = [i for i, ln in enumerate(out) if ln == "{"]
-    end = [i for i, ln in enumerate(out) if ln == "}"]
-    result = json.loads("\n".join(out[start[0] : (end[-1] + 1)]))
-
-    check_dict(result, expected)
-
-    files = os.listdir(project_dir)
-    assert f"{project_name}.tar.gz" not in files
-
-    subprocess.call([str(project_dir / "export.sh")], cwd=project_dir)
-
-    files = os.listdir(project_dir)
-    assert f"{project_name}.tar.gz" in files
-
-
-@pytest.mark.slow
-@pytest.mark.parametrize(
-    "kind", ("Detection", "Segmentation", "Classification")
-)
-def test_algorithm_cli(tmpdir, kind):
-    project_name = f"testalg{kind}"
-
-    files = os.listdir(tmpdir)
-    assert len(files) == 0
-
-    out = subprocess.check_output(
-        [
-            "evalutils",
-            "init",
-            "algorithm",
-            project_name,
-            f"--kind={kind}",
-            "--dev",
-        ],
-        cwd=tmpdir,
-    )
-
-    files = os.listdir(tmpdir)
-    assert project_name in files
-    assert f"Created project {project_name}" in out.decode()
-
-    project_dir = Path(tmpdir) / project_name
-
-    out = subprocess.check_output([str(project_dir / "build.sh")])
-
-    assert "Successfully built" in out.decode()
-    assert f"Successfully tagged {project_name.lower()}:latest" in out.decode()
-    out = subprocess.check_output([str(project_dir / "test.sh")])
-
-    # Grab the results json
-    out = out.decode().splitlines()
-    assert "Tests successfully passed..." in out
-    start = [i for i, ln in enumerate(out) if ln == "["]
-    end = [i for i, ln in enumerate(out) if ln == "]"]
-    result = json.loads("\n".join(out[start[0] : (end[-1] + 1)]))
-
-    with open(
-        Path(__file__).parent.parent
-        / "evalutils"
-        / "templates"
-        / "container"
-        / "{{ cookiecutter.package_name }}"
-        / "algorithm_test"
-        / f"results_{kind.lower()}.json"
-    ) as f:
-        expected = json.load(f)
-
-    assert len(result) == 1
-    assert result == expected
-
-    files = os.listdir(project_dir)
-    assert f"{project_name}.tar.gz" not in files
-
-    subprocess.call([str(project_dir / "export.sh")], cwd=project_dir)
-
-    files = os.listdir(project_dir)
-    assert f"{project_name}.tar.gz" in files
diff --git a/tests/test_validators.py b/tests/test_validators.py
deleted file mode 100644
index 5c41844..0000000
--- a/tests/test_validators.py
+++ /dev/null
@@ -1,104 +0,0 @@
-from pathlib import Path
-
-import pytest
-from pandas import DataFrame
-
-from evalutils.exceptions import ValidationError
-from evalutils.io import ImageIOLoader
-from evalutils.validators import (
-    ExpectedColumnNamesValidator,
-    NumberOfCasesValidator,
-    UniqueImagesValidator,
-    UniquePathIndicesValidator,
-)
-
-
-def test_path_indicies_validator_no_column():
-    df = DataFrame()
-    with pytest.raises(ValidationError):
-        UniquePathIndicesValidator().validate(df=df)
-
-
-def test_path_indicies_vaidator_duplicate_indicies():
-    df = DataFrame({"path": ["01.foo", "1.bar", "03.baz"]})
-    with pytest.raises(ValidationError):
-        UniquePathIndicesValidator().validate(df=df)
-
-
-def test_path_indicies_validator_ok():
-    df = DataFrame({"path": [Path("01.jpg"), Path("11.jpg")]})
-    UniquePathIndicesValidator().validate(df=df)
-
-
-def test_expected_columns_creation():
-    with pytest.raises(ValueError):
-        # noinspection PyTypeChecker
-        ExpectedColumnNamesValidator(expected=())
-
-
-def test_expected_columns_one_undefined():
-    validator = ExpectedColumnNamesValidator(expected=("foo", "bar"))
-    df = DataFrame(columns=["foo"])
-    with pytest.raises(ValidationError):
-        validator.validate(df=df)
-
-
-def test_expected_columns_overdefined():
-    validator = ExpectedColumnNamesValidator(expected=("foo", "bar"))
-    df = DataFrame(columns=["foo", "bar", "baz"])
-    with pytest.raises(ValidationError):
-        validator.validate(df=df)
-
-
-def test_expected_columns_ok():
-    validator = ExpectedColumnNamesValidator(expected=("foo", "bar"))
-    df = DataFrame(columns=["foo", "bar"])
-    validator.validate(df=df)
-
-
-@pytest.fixture(scope="module")
-def images():
-    image1 = ImageIOLoader().load(
-        fname=Path(__file__).parent / "resources" / "images" / "1_mask.png"
-    )[0]
-    image2 = ImageIOLoader().load(
-        fname=Path(__file__).parent / "resources" / "images" / "2_mask.png"
-    )[0]
-    return [image1, image2]
-
-
-def test_unique_images_no_img_col():
-    df = DataFrame(columns=["foo"])
-    with pytest.raises(ValidationError):
-        UniqueImagesValidator().validate(df=df)
-
-
-def test_unique_images_duplicate(images):
-    df = DataFrame([images[0], images[0]])
-    assert df.columns.all() in ["img", "path"]
-
-    with pytest.raises(ValidationError):
-        UniqueImagesValidator().validate(df=df)
-
-
-def test_unique_images_ok(images):
-    df = DataFrame(images)
-    UniqueImagesValidator().validate(df=df)
-
-
-def test_number_of_predictions_badly_configured():
-    with pytest.raises(ValueError):
-        NumberOfCasesValidator(num_cases=-32)
-
-
-def test_number_of_predictions_wrong():
-    df = DataFrame({"foo": ["bar"] * 1337})
-    with pytest.raises(ValidationError):
-        NumberOfCasesValidator(num_cases=1000).validate(df=df)
-    with pytest.raises(ValidationError):
-        NumberOfCasesValidator(num_cases=2000).validate(df=df)
-
-
-def test_number_of_predictions_ok():
-    df = DataFrame({"foo": ["bar"] * 10})
-    NumberOfCasesValidator(num_cases=10).validate(df=df)