TractoGPT: A GPT architecture for White Matter Segmentation
This addresses the problem of brain structural connectivity analysis for neurosurgical planning and neurological disorder studies, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles white matter bundle segmentation by proposing TractoGPT, a GPT-based architecture trained on multiple data representations, which outperforms state-of-the-art methods on metrics like average DICE, Overlap, and Overreach scores.
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.