TractoRC：一种联合束取样注册与聚类的统一概率学习框架
TractoRC: A Unified Probabilistic Learning Framework for Joint Tractography Registration and Clustering
AI总结：
TractoRC通过统一的概率学习框架联合执行束图注册与流线聚类，提升白质组织分析的性能。
摘要：
弥散磁共振成像束取样能够实现活体白质（WM）路径的重建。束取样分析中的两个关键任务包括：1）束图注册，即在个体之间对流线进行对齐；2）流线聚类，即将流线分组为紧凑的纤维束。尽管这两个任务的目标都是捕捉几何上相似的结构以表征一致的白质组织，但它们通常是独立进行的。在本文中，我们提出TractoRC，一种统一的概率框架，能够在单一优化方案中同时执行束图注册和流线聚类，使这两个任务能够利用互补信息。TractoRC学习了一个流线点的潜在嵌入空间，该空间为两个任务提供共享的表示。在该空间中，两个任务都被建模为对结构表示的概率推断：注册学习解剖标志物的分布作为概率关键点以在受试者之间对齐束图，聚类学习流线结构原型以捕捉几何相似性以形成连贯的流线聚类。为了支持有效学习这个共享空间，我们引入了一种转换等变自监督策略来学习几何感知且转换不变的嵌入。实验表明，联合优化注册和聚类在两个任务上均显著优于将它们独立处理的最先进方法。代码将在https://github.com/yishengpoxiao/TractoRC上公开。
英文摘要：
Diffusion MRI tractography enables in vivo reconstruction of white matter (WM) pathways. Two key tasks in tractography analysis include: 1) tractogram registration that aligns streamlines across individuals, and 2) streamline clustering that groups streamlines into compact fiber bundles. Although both tasks share the goal of capturing geometrically similar structures to characterize consistent WM organization, they are typically performed independently. In this work, we propose TractoRC, a unified probabilistic framework that jointly performs tractogram registration and streamline clustering within a single optimization scheme, enabling the two tasks to leverage complementary information. TractoRC learns a latent embedding space for streamline points, which serves as a shared representation for both tasks. Within this space, both tasks are formulated as probabilistic inference over structural representations: registration learns the distribution of anatomical landmarks as probabilistic keypoints to align tractograms across subjects, and clustering learns streamline structural prototypes that capture geometric similarity to form coherent streamline clusters. To support effective learning of this shared space, we introduce a transformation-equivariant self-supervised strategy to learn geometry-aware and transformation-invariant embeddings. Experiments demonstrate that jointly optimizing registration and clustering significantly improves performance in both tasks over state-of-the-art methods that treat them independently. Code will be made publicly available at https://github.com/yishengpoxiao/TractoRC .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
