TractoEmbed: Modular Multi-level Embedding framework for white matter tract segmentation
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913666354380800 |
|---|---|
| author | Goel, Anoushkrit Singh, Bipanjit Joshi, Ankita Jha, Ranjeet Ranjan Ahuja, Chirag Nigam, Aditya Bhavsar, Arnav |
| author_facet | Goel, Anoushkrit Singh, Bipanjit Joshi, Ankita Jha, Ranjeet Ranjan Ahuja, Chirag Nigam, Aditya Bhavsar, Arnav |
| contents | White matter tract segmentation is crucial for studying brain structural connectivity and neurosurgical planning. However, segmentation remains challenging due to issues like class imbalance between major and minor tracts, structural similarity, subject variability, symmetric streamlines between hemispheres etc. To address these challenges, we propose TractoEmbed, a modular multi-level embedding framework, that encodes localized representations through learning tasks in respective encoders. In this paper, TractoEmbed introduces a novel hierarchical streamline data representation that captures maximum spatial information at each level i.e. individual streamlines, clusters, and patches. Experiments show that TractoEmbed outperforms state-of-the-art methods in white matter tract segmentation across different datasets, and spanning various age groups. The modular framework directly allows the integration of additional embeddings in future works. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08187 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TractoEmbed: Modular Multi-level Embedding framework for white matter tract segmentation Goel, Anoushkrit Singh, Bipanjit Joshi, Ankita Jha, Ranjeet Ranjan Ahuja, Chirag Nigam, Aditya Bhavsar, Arnav Computer Vision and Pattern Recognition Artificial Intelligence White matter tract segmentation is crucial for studying brain structural connectivity and neurosurgical planning. However, segmentation remains challenging due to issues like class imbalance between major and minor tracts, structural similarity, subject variability, symmetric streamlines between hemispheres etc. To address these challenges, we propose TractoEmbed, a modular multi-level embedding framework, that encodes localized representations through learning tasks in respective encoders. In this paper, TractoEmbed introduces a novel hierarchical streamline data representation that captures maximum spatial information at each level i.e. individual streamlines, clusters, and patches. Experiments show that TractoEmbed outperforms state-of-the-art methods in white matter tract segmentation across different datasets, and spanning various age groups. The modular framework directly allows the integration of additional embeddings in future works. |
| title | TractoEmbed: Modular Multi-level Embedding framework for white matter tract segmentation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2411.08187 |