Med-ImageTools: An open-source Python package for robust data processing pipelines and curating medical imaging data [version 3; peer review: 1 approved, 1 approved with reservations]

Background Machine learning and AI promise to revolutionize the way we leverage medical imaging data for improving care but require large datasets to train computational models that can be implemented in clinical practice. However, processing large and complex medical imaging datasets remains an ope...

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Published in:F1000 research Vol. 12; p. 118
Main Authors: Kim, Sejin, Kazmierski, Michal, Qu, Kevin, Peoples, Jacob, Nakano, Minoru, Ramanathan, Vishwesh, Marsilla, Joseph, Welch, Mattea, Simpson, Amber, Haibe-Kains, Benjamin
Format: Journal Article
Language:English
Published: England Faculty of 1000 Ltd 2023
F1000 Research Ltd
Subjects:
Artificial intelligence
Data processing
Datasets
deep learning
Diagnostic Imaging
dicom
Digital imaging
Drug dosages
eng
Humans
Image Processing, Computer-Assisted - methods
Learning algorithms
Machine Learning
Magnetic resonance imaging
Mathematical models
Medical imaging
Metadata
Neoplasms - diagnostic imaging
nifti
open source
Radiation therapy
Researchers
Software
Software packages
deep learning
open source
nnunet
data processing
medical imaging
dicom
nifti
ISSN:2046-1402, 2046-1402
Online Access:Get full text
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Summary:Background Machine learning and AI promise to revolutionize the way we leverage medical imaging data for improving care but require large datasets to train computational models that can be implemented in clinical practice. However, processing large and complex medical imaging datasets remains an open challenge. Methods To address this issue, we developed Med-ImageTools, a new Python open-source software package to automate data curation and processing while allowing researchers to share their data processing configurations more easily, lowering the barrier for other researchers to reproduce published works. Use cases We have demonstrated the efficiency of Med-ImageTools across three different datasets, resulting in significantly reduced processing times. Conclusions The AutoPipeline feature will improve the accessibility of raw clinical datasets on public archives, such as the Cancer Imaging Archive (TCIA), the largest public repository of cancer imaging, allowing machine learning researchers to process analysis-ready formats without requiring deep domain knowledge.
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ISSN:2046-1402
2046-1402
DOI:10.12688/f1000research.127142.3