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import datasets |
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import numpy as np |
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import pandas as pd |
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import hickle as hkl |
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_CITATION = """\ |
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@InProceedings{huggingface:dataset, |
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title = {rsna-atd}, |
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author = {Yeow Zi Qin}, |
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year = {2023} |
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} |
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""" |
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_DESCRIPTION = """\ |
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The dataset is the processed version of Kaggle Competition: RSNA 2023 Abdominal Trauma Detection. |
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It comprises of segmentation of 205 series of CT scans with 5 classes (liver, spleen, right_kidney, |
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left_kidney, bowel). |
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""" |
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_NAME = "rsna-atd" |
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_HOMEPAGE = f"https://huggingface.co/datasets/ziq/{_NAME}" |
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_LICENSE = "MIT" |
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_DATA = f"https://huggingface.co/datasets/ziq/{_NAME}/resolve/main/data/" |
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class RSNAATD(datasets.GeneratorBasedBuilder): |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"patient_id": datasets.Value("int64"), |
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"series_id": datasets.Value("int64"), |
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"image": datasets.Array3D(shape=(None, 512, 512), dtype="uint8"), |
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"mask": datasets.Array3D(shape=(None, 512, 512), dtype="uint8"), |
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"aortic_hu": datasets.Value("float64"), |
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"incomplete_organ": datasets.Value("int64"), |
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"bowel_healthy": datasets.Value("int64"), |
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"bowel_injury": datasets.Value("int64"), |
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"extravasation_healthy": datasets.Value("int64"), |
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"extravasation_injury": datasets.Value("int64"), |
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"kidney_healthy": datasets.Value("int64"), |
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"kidney_low": datasets.Value("int64"), |
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"kidney_high": datasets.Value("int64"), |
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"liver_healthy": datasets.Value("int64"), |
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"liver_low": datasets.Value("int64"), |
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"liver_high": datasets.Value("int64"), |
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"spleen_healthy": datasets.Value("int64"), |
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"spleen_low": datasets.Value("int64"), |
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"spleen_high": datasets.Value("int64"), |
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"any_injury": datasets.Value("int64"), |
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} |
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), |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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train_images = dl_manager.download(f"{_DATA}images.tar.gz") |
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train_masks = dl_manager.download(f"{_DATA}masks.tar.gz") |
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metadata = dl_manager.download(f"{_DATA}metadata.csv") |
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train_images = dl_manager.iter_archive(train_images) |
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train_masks = dl_manager.iter_archive(train_masks) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"images": train_images, |
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"masks": train_masks, |
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"metadata": metadata, |
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}, |
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), |
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] |
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def _generate_examples(self, images, masks, metadata): |
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df = pd.read_csv(metadata) |
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for idx, (data, (image_path, image_obj), (mask_path, mask_obj)) in enumerate(zip(df.to_numpy(), images, masks)): |
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image, mask = [hkl.load(image_obj)], [hkl.load(mask_obj)] |
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( |
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patient_id, |
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series_id, |
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aortic_hu, |
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incomplete_organ, |
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bowel_healthy, |
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bowel_injury, |
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extravasation_healthy, |
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extravasation_injury, |
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kidney_healthy, |
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kidney_low, |
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kidney_high, |
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liver_healthy, |
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liver_low, |
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liver_high, |
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spleen_healthy, |
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spleen_low, |
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spleen_high, |
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any_injury, |
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) = data[1:] |
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yield idx, { |
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"patient_id": patient_id, |
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"series_id": series_id, |
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"image": image, |
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"mask": mask, |
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"aortic_hu": aortic_hu, |
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"incomplete_organ": incomplete_organ, |
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"bowel_healthy": bowel_healthy, |
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"bowel_injury": bowel_injury, |
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"extravasation_healthy": extravasation_healthy, |
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"extravasation_injury": extravasation_injury, |
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"kidney_healthy": kidney_healthy, |
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"kidney_low": kidney_low, |
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"kidney_high": kidney_high, |
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"liver_healthy": liver_healthy, |
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"liver_low": liver_low, |
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"liver_high": liver_high, |
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"spleen_healthy": spleen_healthy, |
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"spleen_low": spleen_low, |
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"spleen_high": spleen_high, |
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"any_injury": any_injury, |
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} |
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