SPIDER.py

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""

# Import packages
import csv
import json
import os
from typing import Dict, List, Mapping, Optional, Set, Sequence, Tuple, Union

import numpy as np
import pandas as pd

import datasets
import SimpleITK as sitk

# Define functions
def import_csv_data(filepath: str) -> List[Dict[str, str]]:
    """Import all rows of CSV file."""
    results = []
    with open(filepath, encoding='utf-8') as f:
        reader = csv.DictReader(f)
        for line in reader:
            results.append(line)
    return results

# Define constants
N_PATIENTS = 257
MIN_IVD = 0
MAX_IVD = 9

# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

_HOMEPAGE = "https://zenodo.org/records/10159290"

_LICENSE = """Creative Commons Attribution 4.0 International License \
(https://creativecommons.org/licenses/by/4.0/legalcode)"""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
    "images":"https://zenodo.org/records/10159290/files/images.zip",
    "masks":"https://zenodo.org/records/10159290/files/masks.zip",
    "overview":"https://zenodo.org/records/10159290/files/overview.csv",
    "gradings":"https://zenodo.org/records/10159290/files/radiological_gradings.csv",
}

class CustomBuilderConfig(datasets.BuilderConfig):
    
    def __init__(
        self,
        name: str = 'default',
        version: str = '0.0.0',
        data_dir: Optional[str] = None,
        data_files: Optional[Union[str, Sequence, Mapping]] = None,
        description: Optional[str] = None,
        scan_types: List[str] = ['t1', 't2', 't2_SPACE'],
    ):
        super().__init__(name, version, data_dir, data_files, description)
        self.scan_types = scan_types


class SPIDER(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("1.1.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    BUILDER_CONFIG_CLASS = CustomBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    BUILDER_CONFIGS = [
        CustomBuilderConfig(
            name="all_scan_types",
            version=VERSION,
            description="Use images of all scan types (t1, t2, t2 SPACE)",
            scan_types=['t1', 't2', 't2_SPACE'],
        ),
        CustomBuilderConfig(
            name="t1_scan_types",
            version=VERSION,
            description="Use images of t1 scan types only",
            scan_types=['t1'],
        ),
        CustomBuilderConfig(
            name="t2_scan_types",
            version=VERSION,
            description="Use images of t2 scan types only",
            scan_types=['t2'],
        ),
        CustomBuilderConfig(
            name="t2_SPACE_scan_types",
            version=VERSION,
            description="Use images of t2 SPACE scan types only",
            scan_types=['t2_SPACE'],
        ),
    ]

    DEFAULT_CONFIG_NAME = "all_scan_types"

    def _info(self):
        """
        This method specifies the datasets.DatasetInfo object which contains 
        informations and typings for the dataset.
        """
        features = datasets.Features({
            "patient_id": datasets.Value("string"),
            "scan_type": datasets.Value("string"),
            # "raw_image": datasets.Image(),
            # "numeric_array": datasets.Sequence(datasets.Value("int16")),
            "metadata": {
                "num_vertebrae": datasets.Value(dtype="string"),
                "num_discs": datasets.Value(dtype="string"),
                "sex": datasets.Value(dtype="string"),
                "birth_date": datasets.Value(dtype="string"),
                "AngioFlag": datasets.Value(dtype="string"),
                "BodyPartExamined": datasets.Value(dtype="string"),
                "DeviceSerialNumber": datasets.Value(dtype="string"),
                "EchoNumbers": datasets.Value(dtype="string"),
                "EchoTime": datasets.Value(dtype="string"),
                "EchoTrainLength": datasets.Value(dtype="string"),
                "FlipAngle": datasets.Value(dtype="string"),
                "ImagedNucleus": datasets.Value(dtype="string"),
                "ImagingFrequency": datasets.Value(dtype="string"),
                "InPlanePhaseEncodingDirection": datasets.Value(dtype="string"),
                "MRAcquisitionType": datasets.Value(dtype="string"),
                "MagneticFieldStrength": datasets.Value(dtype="string"),
                "Manufacturer": datasets.Value(dtype="string"),
                "ManufacturerModelName": datasets.Value(dtype="string"),
                "NumberOfPhaseEncodingSteps": datasets.Value(dtype="string"),
                "PercentPhaseFieldOfView": datasets.Value(dtype="string"),
                "PercentSampling": datasets.Value(dtype="string"),
                "PhotometricInterpretation": datasets.Value(dtype="string"),
                "PixelBandwidth": datasets.Value(dtype="string"),
                "PixelSpacing": datasets.Value(dtype="string"),
                "RepetitionTime": datasets.Value(dtype="string"),
                "SAR": datasets.Value(dtype="string"),
                "SamplesPerPixel": datasets.Value(dtype="string"),
                "ScanningSequence": datasets.Value(dtype="string"),
                "SequenceName": datasets.Value(dtype="string"),
                "SeriesDescription": datasets.Value(dtype="string"),
                "SliceThickness": datasets.Value(dtype="string"),
                "SoftwareVersions": datasets.Value(dtype="string"),
                "SpacingBetweenSlices": datasets.Value(dtype="string"),
                "SpecificCharacterSet": datasets.Value(dtype="string"),
                "TransmitCoilName": datasets.Value(dtype="string"),
                "WindowCenter": datasets.Value(dtype="string"),
                "WindowWidth": datasets.Value(dtype="string"),
            },
            "rad_gradings": {
                "IVD label": datasets.Sequence(datasets.Value("string")),
                "Modic": datasets.Sequence(datasets.Value("string")),
                "UP endplate": datasets.Sequence(datasets.Value("string")),
                "LOW endplate": datasets.Sequence(datasets.Value("string")),
                "Spondylolisthesis": datasets.Sequence(datasets.Value("string")),
                "Disc herniation": datasets.Sequence(datasets.Value("string")),
                "Disc narrowing": datasets.Sequence(datasets.Value("string")),
                "Disc bulging": datasets.Sequence(datasets.Value("string")),
                "Pfirrman grade": datasets.Sequence(datasets.Value("string")),
            }
        })

        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """
        This method is tasked with downloading/extracting the data 
        and defining the splits depending on the configuration
        If several configurations are possible (listed in BUILDER_CONFIGS), 
        the configuration selected by the user is in self.config.name
        """

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        paths_dict = dl_manager.download_and_extract(_URLS)
        scan_types = self.config.scan_types
        # scan_types = ['t1'] #TODO: remove
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "paths_dict": paths_dict,
                    "split": "train",
                    "scan_types": scan_types,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "paths_dict": paths_dict,
                    "split": "validate",
                    "scan_types": scan_types,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "paths_dict": paths_dict,
                    "split": "test",
                    "scan_types": scan_types,
                },
            ),
        ]

    def _generate_examples(
        self,
        paths_dict: Dict[str, str],
        split: str = 'train',
        scan_types: List[str] = ['t1', 't2', 't2_SPACE'],
        validate_share: float = 0.3,
        test_share: float = 0.2,
        raw_image: bool = True,
        numeric_array: bool = True,
        metadata: bool = True,
        rad_gradings: bool = True,
        random_seed: int = 9999,
    ) -> Tuple[str, Dict]:
        """
        This method handles input defined in _split_generators to yield 
        (key, example) tuples from the dataset. The `key` is for legacy reasons 
        (tfds) and is not important in itself, but must be unique for each example.
        
        Args
            paths_dict: mapping of data element name to temporary file location
            split: specify training, validation, or testing set;
                options = 'train', 'validate', OR 'test'
            scan_types: list of sagittal scan types to use in examples; 
                options = ['t1', 't2', 't2_SPACE']
            validate_share: float indicating share of data to use for validation;
                must be in range (0.0, 1.0); note that training share is 
                calculated as (1 - validate_share - test_share)
            test_share: float indicating share of data to use for testing;
                must be in range (0.0, 1.0); note that training share is 
                calculated as (1 - validate_share - test_share)
            raw_image: indicates whether to include .mha image file in example
            numeric_array: indicates whether to include numpy numeric array of
                image in example
            metadata: indicates whether to include patient and scanner metadata
                with image example
            rad_gradings: indicates whether to include patient's radiological
                gradings with image example
        
        Yields
            Tuple (unique patient-scan ID, dict of 
        """
        # Set constants
        train_share = (1.0 - validate_share - test_share)
        np.random.seed(int(random_seed))

        # Validate params
        for item in scan_types:
            if item not in ['t1', 't2', 't2_SPACE']:
                raise ValueError(
                    'Scan type "{item}" not recognized as valid scan type.\
                    Verify scan type argument.'
                )
        if split not in ['train', 'validate', 'test']:
            raise ValueError( 
                f'Split argument "{split}" is not recognized. \
                Please enter one of ["train", "validate", "test"]'
            )
        if train_share <= 0.0:
            raise ValueError(
                f'Training share is calculated as (1 - validate_share - test_share) \
                and must be greater than 0. Current calculated value is \
                {round(train_share, 3)}. Adjust validate_share and/or \
                test_share parameters.'
            )
        if validate_share > 1.0 or validate_share < 0.0:
            raise ValueError(
                f'Validation share must be between (0, 1). Current value is \
                {validate_share}.'
            )
        if test_share > 1.0 or test_share < 0.0:
            raise ValueError(
                f'Testing share must be between (0, 1). Current value is \
                {test_share}.'
            )

        # Generate train/validate/test partitions of patient IDs
        partition = np.random.choice(
            ['train', 'dev', 'test'],
            p=[train_share, validate_share, test_share],
            size=N_PATIENTS,
        )
        patient_ids = (np.arange(N_PATIENTS) + 1)
        train_ids = set(patient_ids[partition == 'train'])
        validate_ids = set(patient_ids[partition == 'dev'])
        test_ids = set(patient_ids[partition == 'test'])
        assert len(train_ids.union(validate_ids, test_ids)) == N_PATIENTS

        # Import patient/scanner data and radiological gradings data
        overview_data = import_csv_data(paths_dict['overview'])
        grades_data = import_csv_data(paths_dict['gradings'])
        
        # Convert overview data list of dicts to dict of dicts
        exclude_vars = ['new_file_name', 'subset'] # Original data only lists train and validate
        overview_dict = {}
        for item in overview_data:
            key = item['new_file_name']
            overview_dict[key] = {
                k:v for k,v in item.items() if k not in exclude_vars
            }
        
        # # Determine maximum number of radiological gradings per patient
        # max_ivd = 0
        # for temp_dict_1 in grades_dict.values():
        #     for temp_dict_2 in temp_dict_1:
        #         if int(temp_dict_2['IVD label']) > max_ivd:
        #             max_ivd = int(temp_dict_2['IVD label'])

        # Merge patient records for radiological gradings data
        grades_dict = {}
        for patient_id in patient_ids:
            patient_grades = [
                x for x in grades_data if x['Patient'] == str(patient_id)
            ]
            # Pad so that all patients have same number of IVD observations
            IVD_values = [x['IVD label'] for x in patient_grades]
            for i in range(MIN_IVD, MAX_IVD + 1):
                if str(i) not in IVD_values:
                    patient_grades.append({
                        "Patient": f"{patient_id}",
                        "IVD label": f"{i}",
                        "Modic": "",
                        "UP endplate": "",
                        "LOW endplate": "",
                        "Spondylolisthesis": "",
                        "Disc herniation": "",
                        "Disc narrowing": "",
                        "Disc bulging": "",
                        "Pfirrman grade": "",
                    })
            assert len(patient_grades) == (MAX_IVD - MIN_IVD + 1), "Radiological\
                gradings not padded correctly"
        
            # Convert to sequences
            df = (
                pd.DataFrame(patient_grades)
                .sort_values("IVD label")
                .reset_index(drop=True)
            )
            grades_dict[str(patient_id)] = {
                col:df[col].tolist() for col in df.columns
                if col not in ['Patient']
            }

        # Import image and mask data
        image_files = [
            file for file in os.listdir(os.path.join(paths_dict['images'], 'images'))
            if file.endswith('.mha')
        ]
        assert len(image_files) > 0, "No image files found--check directory path."
        
        mask_files = [
            file for file in os.listdir(os.path.join(paths_dict['masks'], 'masks'))
            if file.endswith('.mha')
        ]
        assert len(mask_files) > 0, "No mask files found--check directory path."
        
        # Filter image and mask data based on scan types
        image_files = [
            file for file in image_files 
            if any(scan_type in file for scan_type in scan_types)
        ]

        mask_files = [
            file for file in mask_files 
            if any(scan_type in file for scan_type in scan_types)
        ]
        
        # Subset train/validation/test partition images and mask files
        if split == 'train':
            subset_ids = train_ids
        elif split == 'validate':
            subset_ids = validate_ids
        elif split == 'test':
            subset_ids = test_ids
        
        image_files = [
            file for file in image_files
            if any(str(patient_id) in file.split('_')[0] for patient_id in subset_ids)
        ]
            
        mask_files = [
            file for file in mask_files
            if any(str(patient_id) in file.split('_')[0] for patient_id in subset_ids)
        ]
        assert len(image_files) == len(mask_files), "The number of image files\
            does not match the number of mask files--verify subsetting operation."
        
        # Shuffle order of patient scans
        # (note that only images need to be shuffled since masks and metadata
        # will be linked to the selected image)
        np.random.shuffle(image_files) 
        
        ## Generate next example
        # ----------------------
        for idx, example in enumerate(image_files):
            print(example, image_files[idx+1])
            
            # Extract linking data
            scan_id = example.replace('.mha', '')
            patient_id = scan_id.split('_')[0]
            scan_type = '_'.join(scan_id.split('_')[1:])

            # Load .mha file
            image_path = os.path.join(paths_dict['images'], 'images', example)
            image = sitk.ReadImage(image_path)
            
            # Convert .mha image to numeric array
            image_array = sitk.GetArrayFromImage(image)
            
            # Extract overview data corresponding to image
            image_overview = overview_dict[scan_id]

            # Extract patient radiological gradings corresponding to patient
            patient_grades_dict = grades_dict[patient_id]

            # Prepare example return dict
            return_dict = {'patient_id':patient_id, 'scan_type':scan_type}
            # if raw_image:
            #     return_dict['raw_image'] = image
            # if numeric_array:
            #     return_dict['numeric_array'] = image_array
            if metadata:
                return_dict['metadata'] = image_overview
            if rad_gradings:
                return_dict['rad_gradings'] = patient_grades_dict
            
            # Yield example
            yield scan_id, return_dict