Upload ColonCancerCTDatasetScript.py

ColonCancerCTDatasetScript.py

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+import pydicom
+from PIL import Image
+import numpy as np
+import io
+import datasets
+import gdown
+import re
+import s3fs
+import random
+
+manifest_url = "https://drive.google.com/uc?id=1JBkQTXeieyN9_6BGdTF_DDlFFyZrGyU6"
+manifest_file = gdown.download(manifest_url, 'manifest_file.s5cmd', quiet=False)
+fs = s3fs.S3FileSystem(anon=True)
+
+_DESCRIPTION = """
+This dataset, curated from the comprehensive collection by the National Cancer Institute (NCI)
+and hosted on AWS, contains over 900,000 colon CT images, along with the corresponding patients'
+information. It is designed to help researcher in developing advanced machine learning models
+for in-depth studies in colon cancer.
+"""
+_HOMEPAGE = "https://imaging.datacommons.cancer.gov/"
+_LICENSE = "https://fairsharing.org/FAIRsharing.0b5a1d"
+_CITATION = """\
+@article{fedorov2021nci,
+  title={NCI imaging data commons},
+  author={Fedorov, Andrey and Longabaugh, William JR and Pot, David
+  and Clunie, David A and Pieper, Steve and Aerts, Hugo JWL and
+  Homeyer, Andr{\'e} and Lewis, Rob and Akbarzadeh, Afshin and
+  Bontempi, Dennis and others},
+  journal={Cancer research},
+  volume={81},
+  number={16},
+  pages={4188--4193},
+  year={2021},
+  publisher={AACR}
+}
+"""
+
+class ColonCancerCTDataset(datasets.GeneratorBasedBuilder):
+  """This dataset script retrieves the dataset using a manifest file from the original dataset's
+  homepage. The file lists the S3 paths for each series of CT images and metadata, guiding the download
+  from AWS. After processing the original content, this dataset will contian the image of the colonography, 
+  image type, study date, series date, manufacturer details, study descriptions, series descriptions, 
+  and patient demographics including sex, age, and pregnancy status.
+  """
+  VERSION = datasets.Version("1.1.0")
+
+  def _info(self):
+    """Returns DatasetInfo."""
+    # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+    return datasets.DatasetInfo(
+        description=_DESCRIPTION,
+        features=datasets.Features(
+            {
+                "image": datasets.Image(),
+                "ImageType": datasets.Sequence(datasets.Value('string')), 
+                "StudyDate": datasets.Value('string'), 
+                "SeriesDate": datasets.Value('string'),
+                "Manufacturer": datasets.Value('string'),
+                "StudyDescription": datasets.Value('string'),
+                "SeriesDescription": datasets.Value('string'),
+                "PatientSex": datasets.Value('string'),
+                "PatientAge": datasets.Value('string'),
+                "PregnancyStatus": datasets.Value('string'),
+                "BodyPartExamined": datasets.Value('string'),
+            }),
+        homepage = _HOMEPAGE,
+        license = _LICENSE,
+        citation = _CITATION
+        )
+
+  def _split_generators(self, dl_manager):
+    """Returns a list of SplitGenerators."""
+    # This method is tasked with extracting the S3 paths of the data and defining the splits 
+    # by shuffling and randomly partitioning the paths in the manifest file.
+    s3_series_paths = []
+    s3_individual_paths = []
+    with open(manifest_file, 'r') as file:
+      for line in file:
+          match = re.search(r'cp (s3://[\S]+) .', line)
+          if match:
+              s3_series_paths.append(match.group(1)[:-2]) # Deleting the '/*' in directories
+    for series in s3_series_paths:
+        for content in fs.ls(series):
+          s3_individual_paths.append(fs.info(content)['Key']) # Retrieve the individual DICOM file's S3 path
+
+    random.shuffle(s3_individual_paths) # Randomly shuffles the paths for partitioning
+
+    # Define the split sizes
+    train_size = int(0.7 * len(s3_individual_paths))
+    val_size = int(0.15 * len(s3_individual_paths))
+    # Split the paths into train, validation, and test sets
+    train_paths = s3_individual_paths[:train_size]
+    val_paths = s3_individual_paths[train_size:train_size + val_size]
+    test_paths = s3_individual_paths[train_size + val_size:]
+
+    return [
+        datasets.SplitGenerator(
+            name=datasets.Split.TRAIN,
+            gen_kwargs={
+                "paths": train_paths,
+                "split": "train"
+            },
+        ),
+        datasets.SplitGenerator(
+            name=datasets.Split.VALIDATION,
+            gen_kwargs={
+                "paths": val_paths,
+                "split": "dev"
+            },
+        ),
+        datasets.SplitGenerator(
+            name=datasets.Split.TEST,
+            gen_kwargs={
+                "paths": test_paths,
+                "split": "test"
+            },
+        ),
+    ]
+
+  def _generate_examples(self, paths, split):
+    """Yields examples."""
+    # This method will yield examples, i.e. rows in the dataset.
+    for path in paths:
+      key = path
+      with fs.open(path, 'rb') as f:
+        dicom_data = pydicom.dcmread(f)
+        pixel_array = dicom_data.pixel_array
+        # Converting pixel array into PNG image
+        # Adjust for MONOCHROME1 to invert the grayscale values
+        if dicom_data.PhotometricInterpretation == "MONOCHROME1":
+          pixel_array = np.max(pixel_array) - pixel_array
+        # Normalize or scale 16-bit or other depth images to 8-bit
+        if pixel_array.dtype != np.uint8:
+            pixel_array = (np.divide(pixel_array, np.max(pixel_array)) * 255).astype(np.uint8)
+        # Convert to RGB if it is not already (e.g., for color images)
+        if len(pixel_array.shape) == 2:
+            im = Image.fromarray(pixel_array, mode="L")  # L mode is for grayscale
+        elif len(pixel_array.shape) == 3 and pixel_array.shape[2] in [3, 4]:
+            im = Image.fromarray(pixel_array, mode="RGB")
+        else:
+            raise ValueError("Unsupported DICOM image format")
+        with io.BytesIO() as output:
+            im.save(output, format="PNG")
+            png_image = output.getvalue()
+        # Extracting metadata
+        ImageType = dicom_data.get("ImageType", "")
+        StudyDate = dicom_data.get("StudyDate", "")
+        SeriesDate = dicom_data.get("SeriesDate", "")
+        Manufacturer = dicom_data.get("Manufacturer", "")
+        StudyDescription = dicom_data.get("StudyDescription", "")
+        SeriesDescription = dicom_data.get("SeriesDescription", "")
+        PatientSex = dicom_data.get("PatientSex", "")
+        PatientAge = dicom_data.get("PatientAge", "")
+        PregnancyStatus = dicom_data.get("PregnancyStatus", "")
+        if PregnancyStatus == None:
+           PregnancyStatus = "None"
+        else:
+           PregnancyStatus = "Yes"
+        BodyPartExamined = dicom_data.get("BodyPartExamined", "")
+        yield key, {"image": png_image, 
+                    "ImageType": ImageType, 
+                    "StudyDate": StudyDate, 
+                    "SeriesDate": SeriesDate, 
+                    "Manufacturer": Manufacturer, 
+                    "StudyDescription": StudyDescription, 
+                    "SeriesDescription": SeriesDescription,
+                    "PatientSex": PatientSex, 
+                    "PatientAge": PatientAge, 
+                    "PregnancyStatus": PregnancyStatus, 
+                    "BodyPartExamined": BodyPartExamined}