Create rsna-2023-abdominal-trauma-detection.py

rsna-2023-abdominal-trauma-detection.py

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+import pandas as pd
+import urllib
+
+import datasets
+
+
+_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 RSNA2023AbdominalTraumaDetectionSegmentation(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="segmentation",
+            version=VERSION,
+            description="This part of the dataset loads the CT scans, segmentation masks, and metadata.",
+        ),
+        datasets.BuilderConfig(
+            name="classification",
+            version=VERSION,
+            description="This part of the dataset loads the CT scans, target labels, and metadata.",
+        ),
+        datasets.BuilderConfig(
+            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.
+
+    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_file = dl_manager.download_and_extract(
+            urllib.parse.urljoin(_URL, "train_series_meta.csv")
+        )
+        dicom_tags_file = dl_manager.download_and_extract(
+            urllib.parse.urljoin(_URL, "train_dicom_tags.parquet")
+        )
+        labels_file = (
+            dl_manager.download_and_extract(urllib.parse.urljoin(_URL, "train.csv"))
+            if self.config.name != "segmentation"
+            else None
+        )
+
+        series_meta_df = pd.read_csv(series_meta_file)
+        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]
+            img_files = dl_manager.download(
+                series_meta_df.apply(
+                    lambda x: urllib.parse.urljoin(
+                        _URL, f"train_images/{x['patient_id']}/{x['series_id']}.nii.gz"
+                    ),
+                    axis=1,
+                ).tolist()
+            )
+            seg_files = dl_manager.download(
+                series_meta_df.apply(
+                    lambda x: urllib.parse.urljoin(
+                        _URL, f"segmentations/{x['series_id']}.nii.gz"
+                    ),
+                    axis=1,
+                ).tolist()
+            )
+        else:
+            img_files = dl_manager.download(
+                series_meta_df.apply(
+                    lambda x: urllib.parse.urljoin(
+                        _URL, f"train_images/{x['patient_id']}/{x['series_id']}.nii.gz"
+                    ),
+                    axis=1,
+                ).tolist()
+            )
+            seg_files = None
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.ALL,
+                gen_kwargs={
+                    "series_ids": series_meta_df["series_id"].tolist(),
+                    "dicom_tags_file": dicom_tags_file,
+                    "series_meta_file": series_meta_file,
+                    "labels_file": labels_file,
+                    "img_files": img_files,
+                    "seg_files": seg_files,
+                },
+            ),
+        ]
+
+    def _generate_examples(
+        self,
+        series_ids,
+        dicom_tags_file,
+        series_meta_file,
+        labels_file,
+        img_files,
+        seg_files,
+    ):
+        series_meta_df = pd.read_csv(series_meta_file)
+        dicom_tags_df = pd.read_csv(dicom_tags_file)[
+            [
+                "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"
+        )
+        labels_df = pd.read_csv(labels_file) if self.config != "segmentation" else None
+
+        if self.config.name == "segmentation":
+            for key, (series_id, img_path, seg_path) in enumerate(
+                zip(series_ids, img_files, seg_files)
+            ):
+                series_meta = (
+                    series_meta_df.loc[series_meta_df["series_id"] == series_id]
+                    .iloc[0]
+                    .to_dict()
+                )
+                yield key, {
+                    "img_path": img_path,
+                    "seg_path": seg_path,
+                    "metadata": {
+                        "series_id": 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_id, img_path, seg_path) in enumerate(
+                zip(series_ids, img_files, seg_files)
+            ):
+                series_meta = (
+                    series_meta_df.loc[series_meta_df["series_id"] == series_id]
+                    .iloc[0]
+                    .to_dict()
+                )
+                patient_id = series_meta["patient_id"]
+                label_data = (
+                    labels_df.loc[labels_df["patient_id"] == patient_id]
+                    .iloc[0]
+                    .to_dict()
+                )
+                yield key, {
+                    "img_path": img_path,
+                    "seg_path": seg_path,
+                    "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": 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:
+            pass