基于3D传输的形态学测量（3D-TBM）用于医学图像分析
3D Transport-based Morphometry (3D-TBM) for medical image analysis
AI总结：
3D-TBM通过传输域特征分析实现3D医学图像的形态学研究，提供数据预处理、最优传输嵌入和可视化方法，支持临床影像研究。
摘要：
传输基于形态学（TBM）作为一种新的框架，为3D医学图像分析提供了新的方法。通过将图像嵌入到传输域中，利用可逆变换，TBM能够通过传输域特征有效地进行分类、回归和其他任务。关键的是，逆向映射使分析结果能够投影回原始图像空间，使研究人员能够以有意义的空间方式直接解释与模型输出相关的临床特征。为了促进TBM在临床影像研究中的更广泛应用，我们提出了3D-TBM，一种用于3D医学图像形态学分析的工具。该框架包括数据预处理、最优传输嵌入的计算以及可视化主要传输方向等分析方法，以及辨别区分方向及相关分析方法。我们还提供了全面的文档和实用教程，以支持对3D-TBM感兴趣的研究人员在自己的医学影像研究中应用。源代码通过PyTransKit公开提供。
英文摘要：
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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