Global Context Is All You Need for Parallel Efficient Tractography Parcellation

• URL: http://arxiv.org/abs/2503.07104v1
• Date: Mon, 10 Mar 2025 09:27:07 GMT
• Title: Global Context Is All You Need for Parallel Efficient Tractography Parcellation
• Authors: Valentin von Bornhaupt, Johannes Grün, and Justus Bisten, Tobias Bauer, Theodor Rüber, Thomas Schultz,
• Abstract summary: We propose PETParc, a new method for Parallel Efficient Tractography Parcellation.<n>PETParc is a transformer-based architecture in which the whole-brain tractogram is randomly partitioned into sub-tractograms.<n>Results are often even better than those of prior methods.
• Score: 0.6596280437011043
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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