Bibliographic Details
| Title: |
TractoGPT: A GPT architecture for White Matter Segmentation |
| Authors: |
Goel, Anoushkrit, Singh, Simroop, Joshi, Ankita, Jha, Ranjeet Ranjan, Ahuja, Chirag, Nigam, Aditya, Bhavsar, Arnav |
| Publication Year: |
2025 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence |
| More Details: |
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.
Comment: Accepted as a conference paper at 23rd IEEE International Symposium on Biomedical Imaging 2025. IEEE holds the copyright for this publication |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2501.15464 |
| Accession Number: |
edsarx.2501.15464 |
| Database: |
arXiv |
