ColonCancerCTDataset.py

import pydicom
from PIL import Image
import numpy as np
import io
import datasets
import gdown
import re
import s3fs
import random

example_manifest_url = "https://drive.google.com/uc?id=1JBkQTXeieyN9_6BGdTF_DDlFFyZrGyU6"
example_manifest_file = gdown.download(example_manifest_url, 'manifest_file.s5cmd', quiet = False)
full_manifest_url = "https://drive.google.com/uc?id=1KP6qxcQoPF4MJdEPNwW7J6BlL_sUJ17j"
full_manifest_file = gdown.download(full_manifest_url, 'full_manifest_file.s5cmd', quiet = False)
fs = s3fs.S3FileSystem(anon=True)

_DESCRIPTION = "This is the description"
_HOMEPAGE = "https://imaging.datacommons.cancer.gov/"
_LICENSE = "https://fairsharing.org/FAIRsharing.0b5a1d"
_CITATION = "National Cancer Institute Imaging Data Commons (IDC) Collections was accessed on DATE from https://registry.opendata.aws/nci-imaging-data-commons"


class ColonCancerCTDataset(datasets.GeneratorBasedBuilder):
  """TODO: Short description of my dataset."""
  VERSION = datasets.Version("1.1.0")

  BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="example", version=VERSION, description="This is a subset of the full dataset for demonstration purposes"),
        datasets.BuilderConfig(name="full_data", version=VERSION, description="This is the complete dataset"),
    ]
  DEFAULT_CONFIG_NAME = "example"

  def _info(self):
    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 SplitGenerators."""
    # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the
    s3_series_paths = []
    s3_individual_paths = []
    if self.config.name == 'example':
        manifest_file = example_manifest_file
    else:
        manifest_file = full_manifest_file
    
    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'])

    random.shuffle(s3_individual_paths)

    # 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."""
    # TODO: 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
        # 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}