DOI: 10.12688/f1000research.188425.1 ISSN: 2046-1402

Med-TDA: Medical Imaging Topological Data Analysis Tool

Dashti A. Ali, Amber L. Simpson
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Background Topological data analysis (TDA) has gained interest in medical image analysis, but there are no standardized definitions or pipelines to benchmark model performance. Current TDA tools and packages are mainly developed for general machine learning applications, there are no packages for medical imaging. In this paper, we introduce medical imaging topological data analysis (Med-TDA) tool, a standardized TDA framework designed to process medical images across different modalities and enables researchers to easily reproduce reported results. Methods Persistent homology (PH), a TDA tool for extracting topological features, is a primary component of the package. Med-TDA is an open-source Python package that provides an end-to-end pipeline for preprocessing medical images, computing PH, vectorizing persistent barcodes, thereby extracting TDA features from medical images. The package is designed to be used either as an importable library or command-line interface (CLI) supporting batch data processing, and can be installed via PyPI or directly from the source. Lastly, we provide tutorials on the GitHub repository, along with comprehensive documentation. Results We evaluated the Med-TDA pipeline on two publicly available medical imaging datasets and compared its performance with that of the standard radiomic features extracted via PyRadiomics pipeline. Overall, the proposed TDA pipeline demonstrated comparable performance to PyRadiomics, with Med-TDA achieving marginally higher performance on one of the datasets. Conclusions We provide Med-TDA, a standardized end-to-end pipeline for computing TDA features from medical images. The experimental results highlight its potential as a practical tool alongside existing radiomic methods in medical image analysis.

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