Datasets:
Tasks:
Image Segmentation
Modalities:
Image
Sub-tasks:
semantic-segmentation
Languages:
English
Size:
10K - 100K
License:
File size: 5,673 Bytes
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import datasets
import pandas as pd
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {RSNA-ATD2023},
author = {Yeow Zi Qin},
year = {2023}
}
"""
_DESCRIPTION = """\
The dataset is the processed version of Kaggle Competition: RSNA 2023 Abdominal Trauma Detection.
It comprises of segmentation of 205 series of CT scans with 5 classes (liver, spleen, right_kidney,
left_kidney, bowel).
"""
_NAME = "RSNA-ATD2023"
_HOMEPAGE = f"https://huggingface.co/datasets/ziq/{_NAME}"
_LICENSE = "MIT"
_DATA = f"https://huggingface.co/datasets/ziq/{_NAME}/resolve/main/data/"
class RSNAATD(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
# "image_path": datasets.Value("string"),
"patient_id": datasets.Value("int64"),
"series_id": datasets.Value("int64"),
"frame_id": datasets.Value("int64"),
"image": datasets.Image(),
"mask": datasets.Image(),
"liver": datasets.Value("int16"),
"spleen": datasets.Value("int16"),
"right_kidney": datasets.Value("int16"),
"left_kidney": datasets.Value("int16"),
"bowel": datasets.Value("int16"),
"aortic_hu": datasets.Value("int16"),
"incomplete_organ": datasets.Value("int16"),
"bowel_healthy": datasets.Value("int16"),
"bowel_injury": datasets.Value("int16"),
"extravasation_healthy": datasets.Value("int16"),
"extravasation_injury": datasets.Value("int16"),
"kidney_healthy": datasets.Value("int16"),
"kidney_low": datasets.Value("int16"),
"kidney_high": datasets.Value("int16"),
"liver_healthy": datasets.Value("int16"),
"liver_low": datasets.Value("int16"),
"liver_high": datasets.Value("int16"),
"spleen_healthy": datasets.Value("int16"),
"spleen_low": datasets.Value("int16"),
"spleen_high": datasets.Value("int16"),
"any_injury": datasets.Value("int16"),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
train_images = dl_manager.download(f"{_DATA}images.tar.gz")
train_masks = dl_manager.download(f"{_DATA}masks.tar.gz")
metadata = dl_manager.download(f"{_DATA}metadata.csv")
train_images = dl_manager.iter_archive(train_images)
train_masks = dl_manager.iter_archive(train_masks)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"images": train_images,
"masks": train_masks,
"metadata": metadata,
},
),
]
# def sort_key(self, x):
# patient_id, series_id, frame_id = (
# x[0][0].replace("images/", "").replace(".png", "").split("_")
# )
# return int(patient_id), int(series_id), int(frame_id)
def _generate_examples(self, images, masks, metadata):
df = pd.read_csv(metadata)
for idx, ((image_path, image), (mask_path, mask)) in enumerate(
zip(images, masks)
):
row = df.loc[df["path"] == image_path.lower().replace("images/", "")]
(
liver,
spleen,
right_kidney,
left_kidney,
bowel,
aortic_hu,
incomplete_organ,
bowel_healthy,
bowel_injury,
extravasation_healthy,
extravasation_injury,
kidney_healthy,
kidney_low,
kidney_high,
liver_healthy,
liver_low,
liver_high,
spleen_healthy,
spleen_low,
spleen_high,
any_injury,
) = row.to_numpy()[0][4:]
yield idx, {
"patient_id": row["patient_id"].values[0],
"series_id": row["series_id"].values[0],
"frame_id": row["frame_id"].values[0],
"image": {"path": image_path, "bytes": image.read()},
"mask": {"path": mask_path, "bytes": mask.read()},
"liver": liver,
"spleen": spleen,
"right_kidney": right_kidney,
"left_kidney": left_kidney,
"bowel": bowel,
"aortic_hu": aortic_hu,
"incomplete_organ": incomplete_organ,
"bowel_healthy": bowel_healthy,
"bowel_injury": bowel_injury,
"extravasation_healthy": extravasation_healthy,
"extravasation_injury": extravasation_injury,
"kidney_healthy": kidney_healthy,
"kidney_low": kidney_low,
"kidney_high": kidney_high,
"liver_healthy": liver_healthy,
"liver_low": liver_low,
"liver_high": liver_high,
"spleen_healthy": spleen_healthy,
"spleen_low": spleen_low,
"spleen_high": spleen_high,
"any_injury": any_injury,
}
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