3D-TBM让医学影像分析结果可空间解读，提升临床可解释性。
3D Transport-based Morphometry (3D-TBM) for medical image analysis
- 通过可逆变换将图像映射到运输域，提取可解释特征
- 支持分类回归，且能将结果投影回原图空间定位关键区域
- 适合关注模型可解释性的医学影像研究者使用
基于传输的形态学分析（TBM）已成为3D医学图像分析的新框架。通过可逆变换将图像嵌入传输域，TBM利用传输域特征实现有效的分类、回归等任务。关键优势在于逆映射可将分析结果投影回原始图像空间，使研究人员能够以空间有意义的方式直接解读与模型输出相关的临床特征。为推动TBM在临床影像研究中的广泛应用，我们提出3D-TBM，一个用于3D医学图像形态分析的工具框架，包含数据预处理、最优传输嵌入计算以及可视化主要传输方向、识别区分性方向及相关分析方法。我们还提供完整文档和实用教程，帮助研究人员在其自身医学影像研究中应用3D-TBM。源代码通过PyTransKit公开。
原文摘要 · Abstract (English)
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.
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