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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.15464 |
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| _version_ | 1866929723514290176 |
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| author | Goel, Anoushkrit Singh, Simroop Joshi, Ankita Jha, Ranjeet Ranjan Ahuja, Chirag Nigam, Aditya Bhavsar, Arnav |
| author_facet | Goel, Anoushkrit Singh, Simroop Joshi, Ankita Jha, Ranjeet Ranjan Ahuja, Chirag Nigam, Aditya Bhavsar, Arnav |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15464 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TractoGPT: A GPT architecture for White Matter Segmentation Goel, Anoushkrit Singh, Simroop Joshi, Ankita Jha, Ranjeet Ranjan Ahuja, Chirag Nigam, Aditya Bhavsar, Arnav Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | TractoGPT: A GPT architecture for White Matter Segmentation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2501.15464 |