Global Context Is All You Need for Parallel Efficient Tractography Parcellation

2025-03-10

AI Summary

To address the efficiency–accuracy trade-off in tract segmentation from whole-brain diffusion MRI—particularly under large-scale data and clinical low-compute settings—this paper proposes the first lightweight, parallel streamline clustering framework leveraging global context modeling. Methodologically: (1) it introduces a flip-invariant streamline embedding with flip augmentation to explicitly encode streamline undirectedness; (2) it employs a Transformer-based architecture to enable global contextual interaction across streamline subsets, eliminating redundant local feature modeling; and (3) it incorporates a randomized sub-fiber graph partitioning scheme to enable highly efficient, GPU-free parallel inference on CPU. Experiments demonstrate that our method achieves over 100× speedup versus TractCloud, enabling real-time execution on standard clinical workstations without GPU acceleration, while matching or surpassing state-of-the-art accuracy on both healthy and pathological datasets.

Technology Category

• Computer Vision: Segmentation
• Data Mining & Knowledge Management: Data Stream Mining
• Machine Learning: Time-Series/Data Streams

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Whole-brain tractography in diffusion MRI is often followed by a parcellation in which each streamline is classified as belonging to a specific white matter bundle, or discarded as a false positive. Efficient parcellation is important both in large-scale studies, which have to process huge amounts of data, and in the clinic, where computational resources are often limited. TractCloud is a state-of-the-art approach that aims to maximize accuracy with a local-global representation. We demonstrate that the local context does not contribute to the accuracy of that approach, and is even detrimental when dealing with pathological cases. Based on this observation, we propose PETParc, a new method for Parallel Efficient Tractography Parcellation. PETParc is a transformer-based architecture in which the whole-brain tractogram is randomly partitioned into sub-tractograms whose streamlines are classified in parallel, while serving as global context for each other. This leads to a speedup of up to two orders of magnitude relative to TractCloud, and permits inference even on clinical workstations without a GPU. PETParc accounts for the lack of streamline orientation either via a novel flip-invariant embedding, or by simply using flips as part of data augmentation. Despite the speedup, results are often even better than those of prior methods. The code and pretrained model will be made public upon acceptance.

Problem

• Improves efficiency in whole-brain tractography parcellation.
• Eliminates need for local context in streamline classification.
• Enables fast, accurate parcellation on limited clinical resources.

Innovation

• Transformer-based architecture for tractography parcellation
• Parallel processing of sub-tractograms for efficiency
• Flip-invariant embedding for streamline orientation