import urllib import numpy as np import pandas as pd import datasets from sklearn.model_selection import train_test_split _CITATION = """\ @InProceedings{huggingface:dataset, title = {RSNA 2023 Abdominal Trauma Detection Dataset}, author={Hong Jia Herng}, year={2023} } @misc{rsna-2023-abdominal-trauma-detection, author = {Errol Colak, Hui-Ming Lin, Robyn Ball, Melissa Davis, Adam Flanders, Sabeena Jalal, Kirti Magudia, Brett Marinelli, Savvas Nicolaou, Luciano Prevedello, Jeff Rudie, George Shih, Maryam Vazirabad, John Mongan}, title = {RSNA 2023 Abdominal Trauma Detection}, publisher = {Kaggle}, year = {2023}, url = {https://kaggle.com/competitions/rsna-2023-abdominal-trauma-detection} } """ _DESCRIPTION = """\ This dataset is the preprocessed version of the dataset from RSNA 2023 Abdominal Trauma Detection Kaggle Competition. It is tailored for segmentation and classification tasks. It contains 3 different configs as described below: - segmentation: 206 instances where each instance includes a CT scan in NIfTI format, a segmentation mask in NIfTI format, and its relevant metadata (e.g., patient_id, series_id, incomplete_organ, aortic_hu, pixel_representation, bits_allocated, bits_stored) - classification: 4711 instances where each instance includes a CT scan in NIfTI format, target labels (e.g., extravasation, bowel, kidney, liver, spleen, any_injury), and its relevant metadata (e.g., patient_id, series_id, incomplete_organ, aortic_hu, pixel_representation, bits_allocated, bits_stored) - classification-with-mask: 206 instances where each instance includes a CT scan in NIfTI format, a segmentation mask in NIfTI format, target labels (e.g., extravasation, bowel, kidney, liver, spleen, any_injury), and its relevant metadata (e.g., patient_id, series_id, incomplete_organ, aortic_hu, pixel_representation, bits_allocated, bits_stored) All CT scans and segmentation masks had already been resampled with voxel spacing (2.0, 2.0, 3.0) and thus its reduced file size. """ _NAME = "rsna-2023-abdominal-trauma-detection" _HOMEPAGE = f"https://huggingface.co/datasets/jherng/{_NAME}" _LICENSE = "MIT" _URL = f"https://huggingface.co/datasets/jherng/{_NAME}/resolve/main/" class RSNA2023AbdominalTraumaDetectionConfig(datasets.BuilderConfig): def __init__(self, **kwargs): self.test_size = kwargs.pop("test_size", 0.1) self.random_state = kwargs.pop("random_state", 42) super(RSNA2023AbdominalTraumaDetectionConfig, self).__init__(**kwargs) class RSNA2023AbdominalTraumaDetection(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [ RSNA2023AbdominalTraumaDetectionConfig( name="segmentation", version=VERSION, description="This part of the dataset loads the CT scans, segmentation masks, and metadata.", ), RSNA2023AbdominalTraumaDetectionConfig( name="classification", version=VERSION, description="This part of the dataset loads the CT scans, target labels, and metadata.", ), RSNA2023AbdominalTraumaDetectionConfig( name="classification-with-mask", version=VERSION, description="This part of the dataset loads the CT scans, segmentation masks, target labels, and metadata.", ), ] DEFAULT_CONFIG_NAME = "classification" # It's not mandatory to have a default configuration. Just use one if it make sense. BUILDER_CONFIG_CLASS = RSNA2023AbdominalTraumaDetectionConfig def _info(self): if self.config.name == "segmentation": features = datasets.Features( { "img_path": datasets.Value("string"), "seg_path": datasets.Value("string"), "metadata": { "series_id": datasets.Value("int32"), "patient_id": datasets.Value("int32"), "incomplete_organ": datasets.Value("bool"), "aortic_hu": datasets.Value("float32"), "pixel_representation": datasets.Value("int32"), "bits_allocated": datasets.Value("int32"), "bits_stored": datasets.Value("int32"), }, } ) elif self.config.name == "classification-with-mask": features = datasets.Features( { "img_path": datasets.Value("string"), "seg_path": datasets.Value("string"), "bowel": datasets.ClassLabel( num_classes=2, names=["healthy", "injury"] ), "extravasation": datasets.ClassLabel( num_classes=2, names=["healthy", "injury"] ), "kidney": datasets.ClassLabel( num_classes=3, names=["healthy", "low", "high"] ), "liver": datasets.ClassLabel( num_classes=3, names=["healthy", "low", "high"] ), "spleen": datasets.ClassLabel( num_classes=3, names=["healthy", "low", "high"] ), "any_injury": datasets.Value("bool"), "metadata": { "series_id": datasets.Value("int32"), "patient_id": datasets.Value("int32"), "incomplete_organ": datasets.Value("bool"), "aortic_hu": datasets.Value("float32"), "pixel_representation": datasets.Value("int32"), "bits_allocated": datasets.Value("int32"), "bits_stored": datasets.Value("int32"), }, } ) else: features = datasets.Features( { "img_path": datasets.Value("string"), "bowel": datasets.ClassLabel( num_classes=2, names=["healthy", "injury"] ), "extravasation": datasets.ClassLabel( num_classes=2, names=["healthy", "injury"] ), "kidney": datasets.ClassLabel( num_classes=3, names=["healthy", "low", "high"] ), "liver": datasets.ClassLabel( num_classes=3, names=["healthy", "low", "high"] ), "spleen": datasets.ClassLabel( num_classes=3, names=["healthy", "low", "high"] ), "any_injury": datasets.Value("bool"), "metadata": { "series_id": datasets.Value("int32"), "patient_id": datasets.Value("int32"), "incomplete_organ": datasets.Value("bool"), "aortic_hu": datasets.Value("float32"), "pixel_representation": datasets.Value("int32"), "bits_allocated": datasets.Value("int32"), "bits_stored": datasets.Value("int32"), }, } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): # segmentation: 206 segmentations and the relevant imgs, train_series_meta.csv, train_dicom_tags.parquet # classification: 4711 all imgs, train.csv, train_series_meta.csv, train_dicom_tags.parquet # classification-with-mask: 206 segmentations and the relevant imgs, train.csv, train_series_meta.csv, train_dicom_tags.parquet series_meta_df = pd.read_csv( dl_manager.download_and_extract( urllib.parse.urljoin(_URL, "train_series_meta.csv") ) ) series_meta_df["img_download_url"] = series_meta_df.apply( lambda x: urllib.parse.urljoin( _URL, f"train_images/{int(x['patient_id'])}/{int(x['series_id'])}.nii.gz", ), axis=1, ) series_meta_df["seg_download_url"] = series_meta_df.apply( lambda x: urllib.parse.urljoin( _URL, f"segmentations/{int(x['series_id'])}.nii.gz" ), axis=1, ) if ( self.config.name == "classification-with-mask" or self.config.name == "segmentation" ): series_meta_df = series_meta_df.loc[series_meta_df["has_segmentation"] == 1] series_meta_df["img_cache_path"] = dl_manager.download( series_meta_df["img_download_url"].tolist() ) series_meta_df["seg_cache_path"] = dl_manager.download( series_meta_df["seg_download_url"].tolist() ) else: series_meta_df["img_cache_path"] = dl_manager.download( series_meta_df["img_download_url"].tolist() ) series_meta_df["seg_cache_path"] = None dicom_tags_df = datasets.load_dataset( "parquet", data_files=urllib.parse.urljoin(_URL, "train_dicom_tags.parquet") )["train"].to_pandas()[ [ "SeriesInstanceUID", "PixelRepresentation", "BitsAllocated", "BitsStored", ] ] dicom_tags_df["SeriesID"] = dicom_tags_df["SeriesInstanceUID"].apply( lambda x: int(x.split(".")[-1]) ) dicom_tags_df = dicom_tags_df.drop(labels=["SeriesInstanceUID"], axis=1) dicom_tags_df = dicom_tags_df.groupby(by=["SeriesID"], as_index=False).first() dicom_tags_df = dicom_tags_df.rename( columns={ "SeriesID": "series_id", "PixelRepresentation": "pixel_representation", "BitsAllocated": "bits_allocated", "BitsStored": "bits_stored", } ) series_meta_df = pd.merge( left=series_meta_df, right=dicom_tags_df, how="inner", on="series_id" ) self.labels_df = ( pd.read_csv( dl_manager.download_and_extract(urllib.parse.urljoin(_URL, "train.csv")) ) if self.config.name != "segmentation" else None ) train_series_meta_df, test_series_meta_df = train_test_split( series_meta_df, test_size=self.config.test_size, random_state=self.config.random_state, shuffle=True ) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepaths": train_series_meta_df[ [ "series_id", "patient_id", "img_cache_path", "seg_cache_path", "incomplete_organ", "aortic_hu", "pixel_representation", "bits_allocated", "bits_stored", ] ].to_dict("records"), }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepaths": test_series_meta_df[ [ "series_id", "patient_id", "img_cache_path", "seg_cache_path", "incomplete_organ", "aortic_hu", "pixel_representation", "bits_allocated", "bits_stored", ] ].to_dict("records"), }, ), ] def _generate_examples( self, filepaths, ): if self.config.name == "segmentation": for key, series_meta in enumerate(filepaths): yield key, { "img_path": series_meta["img_cache_path"], "seg_path": series_meta["seg_cache_path"], "metadata": { "series_id": series_meta["series_id"], "patient_id": series_meta["patient_id"], "incomplete_organ": series_meta["incomplete_organ"], "aortic_hu": series_meta["aortic_hu"], "pixel_representation": series_meta["pixel_representation"], "bits_allocated": series_meta["bits_allocated"], "bits_stored": series_meta["bits_stored"], }, } elif self.config.name == "classification-with-mask": for key, series_meta in enumerate(filepaths): label_data = ( self.labels_df.loc[ self.labels_df["patient_id"] == series_meta["patient_id"] ] .iloc[0] .to_dict() ) yield key, { "img_path": series_meta["img_cache_path"], "seg_path": series_meta["seg_cache_path"], "bowel": np.argmax( [label_data["bowel_healthy"], label_data["bowel_injury"]] ), "extravasation": np.argmax( [ label_data["extravasation_healthy"], label_data["extravasation_injury"], ] ), "kidney": np.argmax( [ label_data["kidney_healthy"], label_data["kidney_low"], label_data["kidney_high"], ] ), "liver": np.argmax( [ label_data["liver_healthy"], label_data["liver_low"], label_data["liver_high"], ] ), "spleen": np.argmax( [ label_data["spleen_healthy"], label_data["spleen_low"], label_data["spleen_high"], ] ), "any_injury": label_data["any_injury"], "metadata": { "series_id": series_meta["series_id"], "patient_id": series_meta["patient_id"], "incomplete_organ": series_meta["incomplete_organ"], "aortic_hu": series_meta["aortic_hu"], "pixel_representation": series_meta["pixel_representation"], "bits_allocated": series_meta["bits_allocated"], "bits_stored": series_meta["bits_stored"], }, } else: for key, series_meta in enumerate(filepaths): label_data = ( self.labels_df.loc[ self.labels_df["patient_id"] == series_meta["patient_id"] ] .iloc[0] .to_dict() ) yield key, { "img_path": series_meta["img_cache_path"], "bowel": np.argmax( [label_data["bowel_healthy"], label_data["bowel_injury"]] ), "extravasation": np.argmax( [ label_data["extravasation_healthy"], label_data["extravasation_injury"], ] ), "kidney": np.argmax( [ label_data["kidney_healthy"], label_data["kidney_low"], label_data["kidney_high"], ] ), "liver": np.argmax( [ label_data["liver_healthy"], label_data["liver_low"], label_data["liver_high"], ] ), "spleen": np.argmax( [ label_data["spleen_healthy"], label_data["spleen_low"], label_data["spleen_high"], ] ), "any_injury": label_data["any_injury"], "metadata": { "series_id": series_meta["series_id"], "patient_id": series_meta["patient_id"], "incomplete_organ": series_meta["incomplete_organ"], "aortic_hu": series_meta["aortic_hu"], "pixel_representation": series_meta["pixel_representation"], "bits_allocated": series_meta["bits_allocated"], "bits_stored": series_meta["bits_stored"], }, }