Brain Tumor Segmentation with Special Emphasis on the Non-Enhancing Brain Tumor Compartment
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866912926311383040 |
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| author | Schaffer, T. Brawanski, A. Wein, S. Tomé, A. M. Lang, E. W. |
| author_facet | Schaffer, T. Brawanski, A. Wein, S. Tomé, A. M. Lang, E. W. |
| contents | A U-Net based deep learning architecture is designed to segment brain tumors as they appear on various MRI modalities. Special emphasis is lent to the non-enhancing tumor compartment. The latter has not been considered anymore in recent brain tumor segmentation challenges like the MICCAI challenges. However, it is considered to be indicative of the survival time of the patient as well as of areas of further tumor growth. Hence it deems essential to have means to automatically delineate its extension within the tumor. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_21703 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Brain Tumor Segmentation with Special Emphasis on the Non-Enhancing Brain Tumor Compartment Schaffer, T. Brawanski, A. Wein, S. Tomé, A. M. Lang, E. W. Computer Vision and Pattern Recognition Machine Learning A U-Net based deep learning architecture is designed to segment brain tumors as they appear on various MRI modalities. Special emphasis is lent to the non-enhancing tumor compartment. The latter has not been considered anymore in recent brain tumor segmentation challenges like the MICCAI challenges. However, it is considered to be indicative of the survival time of the patient as well as of areas of further tumor growth. Hence it deems essential to have means to automatically delineate its extension within the tumor. |
| title | Brain Tumor Segmentation with Special Emphasis on the Non-Enhancing Brain Tumor Compartment |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2602.21703 |