TrackletGPT: A Language-like GPT Framework for White Matter Tract Segmentation

2026-01-20

AI Summary

White matter tract segmentation is crucial for brain connectomics and clinical applications, yet existing methods suffer from limited generalizability due to inter-subject variability and differences in acquisition protocols. This work proposes TrackletGPT—the first end-to-end framework to adapt a GPT-like architecture for tract segmentation—by decomposing streamlines into tracklet sequences and leveraging self-attention mechanisms together with 3D structural priors to effectively model both the commonalities and variabilities of fiber tracts. Evaluated on the TractoInferno and Human Connectome Project (HCP) datasets, TrackletGPT consistently outperforms current state-of-the-art methods, achieving superior cross-dataset performance in terms of DICE, Overlap, and Overreach metrics. This study provides the first demonstration of the generalizability and efficacy of language-model-inspired paradigms in neuroimaging segmentation tasks.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

White Matter Tract Segmentation is imperative for studying brain structural connectivity, neurological disorders and neurosurgery. This task remains complex, as tracts differ among themselves, across subjects and conditions, yet have similar 3D structure across hemispheres and subjects. To address these challenges, we propose TrackletGPT, a language-like GPT framework which reintroduces sequential information in tokens using tracklets. TrackletGPT generalises seamlessly across datasets, is fully automatic, and encodes granular sub-streamline segments, Tracklets, scaling and refining GPT models in Tractography Segmentation. Based on our experiments, TrackletGPT outperforms state-of-the-art methods on average DICE, Overlap and Overreach scores on TractoInferno and HCP datasets, even on inter-dataset experiments.

Problem

• White Matter Tract Segmentation
• tractography
• structural connectivity
• neurological disorders
• neurosurgery

Innovation

• TrackletGPT
• white matter tract segmentation
• tracklets
• GPT framework
• cross-dataset generalization