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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"],
},
}
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