Self-calibrated convolution towards glioma segmentation
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866916118206087168 |
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| author | Salvagnini, Felipe C. R. Barbosa, Gerson O. Falcao, Alexandre X. Santos, Cid A. N. |
| author_facet | Salvagnini, Felipe C. R. Barbosa, Gerson O. Falcao, Alexandre X. Santos, Cid A. N. |
| contents | Accurate brain tumor segmentation in the early stages of the disease is crucial for the treatment's effectiveness, avoiding exhaustive visual inspection of a qualified specialist on 3D MR brain images of multiple protocols (e.g., T1, T2, T2-FLAIR, T1-Gd). Several networks exist for Glioma segmentation, being nnU-Net one of the best. In this work, we evaluate self-calibrated convolutions in different parts of the nnU-Net network to demonstrate that self-calibrated modules in skip connections can significantly improve the enhanced-tumor and tumor-core segmentation accuracy while preserving the wholetumor segmentation accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_05218 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Self-calibrated convolution towards glioma segmentation Salvagnini, Felipe C. R. Barbosa, Gerson O. Falcao, Alexandre X. Santos, Cid A. N. Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Accurate brain tumor segmentation in the early stages of the disease is crucial for the treatment's effectiveness, avoiding exhaustive visual inspection of a qualified specialist on 3D MR brain images of multiple protocols (e.g., T1, T2, T2-FLAIR, T1-Gd). Several networks exist for Glioma segmentation, being nnU-Net one of the best. In this work, we evaluate self-calibrated convolutions in different parts of the nnU-Net network to demonstrate that self-calibrated modules in skip connections can significantly improve the enhanced-tumor and tumor-core segmentation accuracy while preserving the wholetumor segmentation accuracy. |
| title | Self-calibrated convolution towards glioma segmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2402.05218 |