3D Transport-based Morphometry (3D-TBM) for medical image analysis
This provides a framework for clinical imaging researchers to analyze and interpret 3D medical images more effectively, though it appears incremental as an extension of existing TBM methods.
The authors tackled the challenge of interpreting 3D medical image analysis by introducing 3D-TBM, a tool that embeds images into a transport domain for tasks like classification and regression, with an inverse mapping to project results back into the original image space for spatial interpretation.
Transport-Based Morphometry (TBM) has emerged as a new framework for 3D medical image analysis. By embedding images into a transport domain via invertible transformations, TBM facilitates effective classification, regression, and other tasks using transport-domain features. Crucially, the inverse mapping enables the projection of analytic results back into the original image space, allowing researchers to directly interpret clinical features associated with model outputs in a spatially meaningful way. To facilitate broader adoption of TBM in clinical imaging research, we present 3D-TBM, a tool designed for morphological analysis of 3D medical images. The framework includes data preprocessing, computation of optimal transport embeddings, and analytical methods such as visualization of main transport directions, together with techniques for discerning discriminating directions and related analysis methods. We also provide comprehensive documentation and practical tutorials to support researchers interested in applying 3D-TBM in their own medical imaging studies. The source code is publicly available through PyTransKit.