Datasets:
Tasks:
Image Classification
Sub-tasks:
multi-label-image-classification
Languages:
English
Size:
100B<n<1T
License:
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} |