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TractoRC: A Unified Probabilistic Learning Framework for Joint Tractography Registration and Clustering

Yijie Li, Xi Zhu, Junyi Wang, Ye Wu, Lauren J. O'Donnell, Fan Zhang

TL;DR

This work proposes 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.

Abstract

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 .

TractoRC: A Unified Probabilistic Learning Framework for Joint Tractography Registration and Clustering

TL;DR

This work proposes 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.

Abstract

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 .
Paper Structure (21 sections, 3 equations, 3 figures, 3 tables)

This paper contains 21 sections, 3 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Network Architecture
  4. Tractogram Embedding Subnetwork.
  5. Tractogram Registration Subnetwork.
  6. Streamline Clustering Subnetwork.
  7. Joint Registration-Clustering Model Training
  8. Self-Supervised Pretraining.
  9. Joint Optimization.
  10. Model Inference
  11. Tractogram Registration.
  12. Streamline Clustering:
  13. Implementation Details
  14. Experimental and Results
  15. Dataset and Preprocessing
  16. ...and 6 more sections

Figures (3)

  • Figure 1: Overview of the proposed TractoRC framework.
  • Figure 2: Visualization of registration performance across different methods. The aligned tractogram is shown in green, and the target tractogram is shown in yellow.
  • Figure 3: Visualization of clustering performance across different methods.