3D Transport-based Morphometry (3D-TBM) for medical image analysis

2026-02-06

AI Summary

This work proposes the first end-to-end 3D Transport-Based Morphometry (3D-TBM) framework to address the challenge of interpretable morphological analysis in 3D medical imaging. Built upon optimal transport theory, the method constructs an invertible embedding that maps 3D images into a transport domain for classification and regression, while enabling back-projection of discriminative features to the original image space for spatially explicit clinical interpretability. Key contributions include visualization of principal transport directions, identification of discriminative morphological patterns, and the release of PyTransKit—an open-source platform offering a complete toolchain encompassing preprocessing, embedding, analysis, and visualization, accompanied by tutorials to facilitate adoption of TBM in medical imaging research.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Matrix & Tensor Methods
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Security and Privacy: Data transparency and provenance

Abstract

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.

Problem

• 3D medical image analysis
• Transport-Based Morphometry
• morphological analysis
• optimal transport
• image interpretation

Innovation

• 3D Transport-based Morphometry
• optimal transport
• invertible transformation
• medical image analysis
• morphological analysis