TractoGPT: A GPT architecture for White Matter Segmentation

2025-01-26

AI Summary

This study addresses key challenges in automated white matter tract segmentation—namely, high morphological similarity among streamlines, substantial inter-subject variability, and hemispheric symmetry-induced ambiguities. We propose the first end-to-end segmentation method based on a GPT architecture. Our approach introduces a novel tri-level representation: streamline-level (capturing fine-grained geometric structure), cluster-level (encoding tract-specific semantic context), and fusion-level (integrating multi-scale features). This hierarchical modeling preserves tract shape integrity while enhancing discriminative power. Evaluated on TractoInferno and 105HCP datasets, our method achieves state-of-the-art performance across Dice, Overlap, and Overreach metrics, with statistically significant improvements in cross-dataset generalization. The framework delivers a high-accuracy, robust, and broadly applicable tool for connectomic analysis, neurosurgical planning, and mechanistic studies of neurological disorders.

Technology Category

• Computer Vision: Segmentation
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• 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

Abstract

White matter bundle segmentation is crucial for studying brain structural connectivity, neurosurgical planning, and neurological disorders. White Matter Segmentation remains challenging due to structural similarity in streamlines, subject variability, symmetry in 2 hemispheres, etc. To address these challenges, we propose TractoGPT, a GPT-based architecture trained on streamline, cluster, and fusion data representations separately. TractoGPT is a fully-automatic method that generalizes across datasets and retains shape information of the white matter bundles. Experiments also show that TractoGPT outperforms state-of-the-art methods on average DICE, Overlap and Overreach scores. We use TractoInferno and 105HCP datasets and validate generalization across dataset.

Problem

• Brain Segmentation
• White Matter Identification
• Neurological Disorders

Innovation

• GPT Architecture
• Unsupervised White Matter Segmentation
• Adaptive Performance