Med-ImageTools: An open-source Python package for robust data processing pipelines and curating medical imaging data [version 1; peer review: 2 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 op...

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Vydané v:F1000 research Ročník 12; s. 118
Hlavní autori: Kim, Sejin, Kazmierski, Michal, Qu, Kevin, Peoples, Jacob, Nakano, Minoru, Ramanathan, Vishwesh, Marsilla, Joseph, Welch, Mattea, Simpson, Amber, Haibe-Kains, Benjamin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: 2023
Predmet:
deep learning
open source
nnunet
data processing
medical imaging
dicom
nifti
ISSN:2046-1402, 2046-1402
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Popis
Shrnutí: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.
ISSN:2046-1402
2046-1402
DOI:10.12688/f1000research.127142.1