# 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: Add a description here.""" #TODO # Import packages import csv import os from typing import Dict, List, Mapping, Optional, Sequence, Set, Tuple, Union import numpy as np import pandas as pd import datasets import skimage 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 def subset_file_list(all_files: List[str], subset_ids: Set[int]): """Subset files pertaining to individuals in person_ids arg.""" return [ file for file in all_files if any(str(id_val) == file.split('_')[0] for id_val in subset_ids) ] def standardize_3D_image( image: np.ndarray, resize_shape: Tuple[int, int, int] ) -> np.ndarray: """Aligns dimensions of image to be (height, width, channels) and resizes images to values specified in resize_shape.""" # Align height, width, channel dims if image.shape[0] < image.shape[2]: image = np.transpose(image, axes=[1, 2, 0]) # Resize image image = skimage.transform.resize(image, resize_shape) # Rescale to UInt8 type image = skimage.img_as_ubyte(image) return image # Define constants N_PATIENTS = 218 MIN_IVD = 0 MAX_IVD = 9 DEFAULT_SCAN_TYPES = ['t1', 't2', 't2_SPACE'] DEFAULT_RESIZE = (512, 512, 30) _CITATION = """\ @misc{vandergraaf2023lumbar, title={Lumbar spine segmentation in MR images: a dataset and a public benchmark}, author={Jasper W. van der Graaf and Miranda L. van Hooff and \ Constantinus F. M. Buckens and Matthieu Rutten and \ Job L. C. van Susante and Robert Jan Kroeze and \ Marinus de Kleuver and Bram van Ginneken and Nikolas Lessmann}, year={2023}, eprint={2306.12217}, archivePrefix={arXiv}, primaryClass={eess.IV} } """ # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ This is a large publicly available multi-center lumbar spine magnetic resonance \ imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral \ discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 \ MRI series from 218 studies of 218 patients with a history of low back pain. \ The data was collected from four different hospitals. There is an additional \ hidden test set, not available here, used in the accompanying SPIDER challenge \ on spider.grand-challenge.org. We share this data to encourage wider \ participation and collaboration in the field of spine segmentation, and \ ultimately improve the diagnostic value of lumbar spine MRI. This file also provides the biological sex for all patients and the age for \ the patients for which this was available. It also includes a number of \ scanner and acquisition parameters for each individual MRI study. The dataset \ also comes with radiological gradings found in a separate file for the \ following degenerative changes: 1.    Modic changes (type I, II or III) 2.    Upper and lower endplate changes / Schmorl nodes (binary) 3.    Spondylolisthesis (binary) 4.    Disc herniation (binary) 5.    Disc narrowing (binary) 6.    Disc bulging (binary) 7.    Pfirrman grade (grade 1 to 5). All radiological gradings are provided per IVD level.""" _HOMEPAGE = "https://zenodo.org/records/10159290" _LICENSE = """Creative Commons Attribution 4.0 International License \ (https://creativecommons.org/licenses/by/4.0/legalcode)""" _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] = DEFAULT_SCAN_TYPES, resize_shape: Tuple[int, int, int] = DEFAULT_RESIZE, shuffle: bool = True, ): super().__init__(name, version, data_dir, data_files, description) self.scan_types = self.validate_scan_types(scan_types) self.resize_shape = resize_shape self.shuffle = shuffle def validate_scan_types(self, scan_types): 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.' ) return scan_types class SPIDER(datasets.GeneratorBasedBuilder): """TODO: Short description of my dataset.""" #TODO # Class attributes DEFAULT_WRITER_BATCH_SIZE = 16 # PyArrow default is too large for image data VERSION = datasets.Version("1.1.0") BUILDER_CONFIG_CLASS = CustomBuilderConfig BUILDER_CONFIGS = [ CustomBuilderConfig( name="default", description="Load the full dataset", ), CustomBuilderConfig( name="demo", description="Generate 10 examples for demonstration", ) ] DEFAULT_CONFIG_NAME = "default" # def __init__( # self, # *args, # scan_types: List[str] = DEFAULT_SCAN_TYPES, # resize_shape: Tuple[int, int, int] = DEFAULT_RESIZE, # shuffle: bool = True, # **kwargs, # ): # super().__init__(*args, **kwargs) # self.scan_types = self.config.scan_types # self.resize_shape = self.config.resize_shape # self.shuffle = self.config.shuffle def _info(self): """Specify datasets.DatasetInfo object containing information and typing for the dataset.""" features = datasets.Features({ "patient_id": datasets.Value("string"), "scan_type": datasets.Value("string"), "image": datasets.Image(), "mask": datasets.Image(), # "image": datasets.Array3D(shape=self.config.resize_shape, dtype='uint8'), # "mask": datasets.Array3D(shape=self.config.resize_shape, dtype='uint8'), "image_path": datasets.Value("string"), "mask_path": datasets.Value("string"), "metadata": { "num_vertebrae": datasets.Value(dtype="string"), #TODO: more specific types "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"), "OrigSubset":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( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators( self, dl_manager, validate_share: float = 0.2, test_share: float = 0.2, random_seed: int = 9999, ): """ Download and extract data and define splits based on configuration. Args dl_manager: HuggingFace datasets download manager (automatically supplied) 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) random_seed: seed for random draws of train/validate/test patient ids """ # Set constants train_share = (1.0 - validate_share - test_share) np.random.seed(int(random_seed)) # Validate params 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}.' ) # Download images (returns dictionary to local cache) paths_dict = dl_manager.download_and_extract(_URLS) # Get list of image and mask data files 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 files based on scan types image_files = [ file for file in image_files if any(scan_type in file for scan_type in self.config.scan_types) ] mask_files = [ file for file in mask_files if any(scan_type in file for scan_type in self.config.scan_types) ] # 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 # Subset train/validation/test partition images and mask files train_image_files = subset_file_list(image_files, train_ids) validate_image_files = subset_file_list(image_files, validate_ids) test_image_files = subset_file_list(image_files, test_ids) train_mask_files = subset_file_list(mask_files, train_ids) validate_mask_files = subset_file_list(mask_files, validate_ids) test_mask_files = subset_file_list(mask_files, test_ids) assert len(train_image_files) == len(train_mask_files) assert len(validate_image_files) == len(validate_mask_files) assert len(test_image_files) == len(test_mask_files) # 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 } overview_dict[key]['OrigSubset'] = item['subset'] # Change name to original subset # 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'] } # DEMO configuration: subset first 10 examples if self.config.name == "demo": train_image_files = train_image_files[:10] train_mask_files = train_mask_files[:10] validate_image_files = validate_image_files[:10] validate_mask_files = validate_mask_files[:10] test_image_files = test_image_files[:10] test_mask_files = test_mask_files[:10] return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "paths_dict": paths_dict, "image_files": train_image_files, "mask_files": train_mask_files, "overview_dict": overview_dict, "grades_dict": grades_dict, "resize_shape": self.config.resize_shape, "shuffle": self.config.shuffle, }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "paths_dict": paths_dict, "image_files": validate_image_files, "mask_files": validate_mask_files, "overview_dict": overview_dict, "grades_dict": grades_dict, "resize_shape": self.config.resize_shape, "shuffle": self.config.shuffle, }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "paths_dict": paths_dict, "image_files": test_image_files, "mask_files": test_mask_files, "overview_dict": overview_dict, "grades_dict": grades_dict, "resize_shape": self.config.resize_shape, "shuffle": self.config.shuffle, }, ), ] def _generate_examples( self, paths_dict: Dict[str, str], image_files: List[str], mask_files: List[str], overview_dict: Dict, grades_dict: Dict, resize_shape: Tuple[int, int, int], shuffle: bool, ) -> 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. """ # Shuffle order of patient scans # (note that only images need to be shuffled since masks and metadata # will be linked to the selected image) if shuffle: np.random.shuffle(image_files) ## Generate next example # ---------------------- for idx, example in enumerate(image_files): # Extract linking data scan_id = example.replace('.mha', '') patient_id = scan_id.split('_')[0] scan_type = '_'.join(scan_id.split('_')[1:]) # Load .mha image file image_path = os.path.join(paths_dict['images'], 'images', example) image = sitk.ReadImage(image_path) # Convert .mha image to original size numeric array image_array_original = sitk.GetArrayFromImage(image) # Convert .mha image to standardized numeric array image_array_standardized = standardize_3D_image( image_array_original, resize_shape, ) # Load .mha mask file mask_path = os.path.join(paths_dict['masks'], 'masks', example) mask = sitk.ReadImage(mask_path) # Convert .mha mask to original size numeric array mask_array_original = sitk.GetArrayFromImage(mask) # Convert .mha mask to standardized numeric array mask_array_standardized = standardize_3D_image( mask_array_original, resize_shape, ) # 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, 'image':image_array_standardized, 'mask':mask_array_standardized, 'image_path':image_path, 'mask_path':mask_path, 'metadata':image_overview, 'rad_gradings':patient_grades_dict, } # Yield example yield scan_id, return_dict