mrsegmentator 1.3.1
Multi-Modality Segmentation of 40 Classes in MRI and CT
Requires Python: <3.13,>=3.9

<h2 align="center"> MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT </h2> 

***

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> Detect and segment 40 classes in MRI and CT of the abdominal / pelvic / thorax region


Contrary to CT scans, where tools for automatic multi-structure segmentation are quite mature, segmentation tasks in MRI scans are often either focused on the brain region or on a subset of few organs in other body regions. MRSegmentator aims to extend this and accurately segment 40 organs and structures in human MRI scans of the abdominal, pelvic and thorax regions. The segmentation works well on different sequence types, including T1- and T2-weighted, Dixon sequences and even CT images. 


### Updates (v1.3.0)
- Our paper has been published! Read more about MRSegmentator in Radiology AI: https://doi.org/10.1148/ryai.240777
- We support .mha and .nrrd files now
- We support DICOM now: If a DICOM directory is used as input a corresponding DICOM SEG will be generated.

Understand the model in depth by reading our [Evaluation](evaluation) section. 
 

![Sample Image](images/SampleSegmentation.png)

## Installation
Install MRSegmentator with pip:
```bash
# Create virtual environment
conda create -n mrseg python=3.11 pip
conda activate mrseg

# Install MRSegmentator
python -m pip install mrsegmentator
```
If the installed pytorch version is not compatible to your system, you might need to install it manually. Please refer to [PyTorch](https://pytorch.org/get-started/locally/). MRSegmentator requires torch <= 2.3.1.

## Docker Image
You can run an MRSegmentator Docker image directly from [MHub](https://mhub.ai/models/mrsegmentator).
```bash
$input_dir=/path/to/input
$output_dir=/path/to/output

docker run --rm -t --gpus all --network=none -v $input_dir:/app/data/input_data:ro -v $output_dir:/app/data/output_data mhubai/mrsegmentator:latest --workflow default
```


## Inference
MRSegmentator segments all `.nii/.nii.gz/.mha/.nrrd` files in an input directory and writes segmentations to the specified output directory. To speed up segmentation you can increase the `--batchsize` or select a single model for inference with `--fold 0`.
MRSegmentator requires a lot of memory and can run into OutOfMemory exceptions when used on very large images. You can reduce memory usage by setting ```--split_level``` to 1 or 2. Be aware that this increases runtime. Read more about the options in the [Evaluation](evaluation) section. 

**New**: You can now also run MRSegmentator on DICOM directories, in which case it produces a  DICOM SEG. (Make sure that there is only a single series UID in the directory). You can also convert previously created segmentations back to DICOM SEG (see [dcm_helper](DCM_Helper_README.md)).

```bash
mrsegmentator --input <file / directory / DICOM directory>
```

Options:
```bash
-i, --input <str> [required] # input directory or file

--outdir <str>  # output directory
--fold <int> # use only a single model for inference 
--postfix <str> # postfix that will be added to segmentations, default: "seg"
--cpu_only # don't use a gpu

# memory (mutually exclusive)
--batchsize <int> # number of images that can be loaded to memory at the same time, default: 8 
--split_level <int> # split images to reduce memory usage. Images are split recursively: A split level of x will produce 2^x smaller images

# debugging
--log_level <["DEBUG", "INFO", "WARNING", "ERROR"]> # Default: INFO
--no_tqdm # disable tqdm progress bars

# experimental
--split_margin <int> # split images with an overlap of 2xmargin to avoid hard cutt-offs between segmentations of top and bottom image, default: 3
--nproc <int> # number of processes
--nproc_export <int> # number of processes for exporting the segmentations
```

## Python API
```python
from mrsegmentator import inference
import os

outdir = "outputdir"
images = [f.path for f in os.scandir("image_dir")]

inference.infer(images, outdir)
```

## Change Path to Weights
MRSegmentator will automatically download its weights and save them in `.conda/envs/<name>/lib/python3.11/site-packages/mrsegmentator/weights`.
This enables easy uninstallation including the weights, should you decide to clean your virtual environments.

If you have multiple environments set the MRSEG_WEIGHTS_PATH variable to prevent downloading multiple copies. Alternatively you can save the weights in a set location on your machine. For this you need to:
1. Download them from [releases](https://github.com/hhaentze/MRSegmentator/releases/tag/v1.2.0) or move them from your conda environment
2. Unzip the files
3. Set the variable "MRSEG_WEIGHTS_PATH" to your weights directory
(e.g.; `export MRSEG_WEIGHTS_PATH="/home/user/weights`)


## How To Cite
If you use our work in your research, please cite our article: https://doi.org/10.1148/ryai.240777.

## Class details

![Sample Image](images/Anatomy_40_classes.png)

|Index|Class|
| :-------- | :------- |
| 0 | background |
| 1 | spleen |
| 2 | right_kidney |
| 3 | left_kidney |
| 4 | gallbladder |
| 5 | liver |
| 6 | stomach |
| 7 | pancreas |
| 8 | right_adrenal_gland |
| 9 | left_adrenal_gland |
| 10 | left_lung |
| 11 | right_lung |
| 12 | heart |
| 13 | aorta |
| 14 | inferior_vena_cava |
| 15 | portal_vein_and_splenic_vein |
| 16 | left_iliac_artery |
| 17 | right_iliac_artery |
| 18 | left_iliac_vena |
| 19 | right_iliac_vena |
| 20 | esophagus |
| 21 | small_bowel |
| 22 | duodenum |
| 23 | colon |
| 24 | urinary_bladder |
| 25 | spine |
| 26 | sacrum |
| 27 | left_hip |
| 28 | right_hip |
| 29 | left_femur |
| 30 | right_femur |
| 31 | left_autochthonous_muscle |
| 32 | right_autochthonous_muscle |
| 33 | left_iliopsoas_muscle |
| 34 | right_iliopsoas_muscle |
| 35 | left_gluteus_maximus |
| 36 | right_gluteus_maximus |
| 37 | left_gluteus_medius |
| 38 | right_gluteus_medius |
| 39 | left_gluteus_minimus |
| 40 | right_gluteus_minimus |

##  Acknowledgements
This work was in large parts funded by the Wilhelm Sander Foundation.
Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Health and Digital Executive Agency (HADEA). Neither the European Union nor the granting authority can be held responsible for them.

![Funding Statement](images/eu_funding_statement.png)
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# Changelog

<!--next-version-placeholder-->

## v1.3.1 (11/08/2025)

### Fix
- fixed incorrect file names that occured when mapping DICOM to NIfTI

## v1.3.0 (09/08/2025)

### Feature
- Automatically run on CPU if no GPU was detected
- Add support for .mha and .nrrd
- Add support for DICOM and DICOM SEG
- Enable better logging:
    - control log lebel (DEBUG, INFO, WARNING)
    - add flag --no_tqdm to disable progress bars
    - remove --verbose flag (replaced by --log_level DEBUG)

## v1.2.3 (05/02/2025)

### Feature
- Print image and subvolume size if splitting is used
- Add option --split_margin to allow to change overlap between splitted volumes

### Fix
- Set pytotch version to <= 2.3.1
- Set python version to < 3.13
- ==> Fixes toch.pickle error due to updated dependency- Supress torch.load future warning, introduced by nnunet
- Increase default split_margin from 2 to 3

## v1.2.2 (11/12/2024)

### Feature
- Print segmentation time after finishing

### Fix
- Supress torch.load future warning, introduced by nnunet
- Print version number of custom weight directories, if they are specified

## v1.2.0 (22/08/2024)

### Feature
- Add NAKO data to training pipeline
- Update weights

### Fix
- Make ensemble prediction default for Python API

___

## v1.1.2 (24/06/2024)

### Fix
- Change python_requires from 3.11 to 3.9
- Remove monai dependency


## v1.1.0 (18/05/2024)

### Feature
- Update model weights with weights trained by `nnUNetTrainerNoMirroring`

### Fix
- Remove postprocessing `remap_left_right(...)`. It is not needed anymore.

___
## v1.0.0 (10/05/2024)
- First release of MRSegmentator