Postoperative glioblastoma segmentation: Development of a fully automated pipeline using deep convolutional neural networks and comparison with currently available models
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arXiv
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| Autores principales: | , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
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| _version_ | 1866911844830019584 |
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| author | Cepeda, Santiago Romero, Roberto Garcia-Perez, Daniel Blasco, Guillermo Luppino, Luigi Tommaso Kuttner, Samuel Arrese, Ignacio Solheim, Ole Eikenes, Live Karlberg, Anna Perez-Nunez, Angel Escudero, Trinidad Hornero, Roberto Sarabia, Rosario |
| author_facet | Cepeda, Santiago Romero, Roberto Garcia-Perez, Daniel Blasco, Guillermo Luppino, Luigi Tommaso Kuttner, Samuel Arrese, Ignacio Solheim, Ole Eikenes, Live Karlberg, Anna Perez-Nunez, Angel Escudero, Trinidad Hornero, Roberto Sarabia, Rosario |
| contents | Accurately assessing tumor removal is paramount in the management of glioblastoma. We developed a pipeline using MRI scans and neural networks to segment tumor subregions and the surgical cavity in postoperative images. Our model excels in accurately classifying the extent of resection, offering a valuable tool for clinicians in assessing treatment effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_11725 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Postoperative glioblastoma segmentation: Development of a fully automated pipeline using deep convolutional neural networks and comparison with currently available models Cepeda, Santiago Romero, Roberto Garcia-Perez, Daniel Blasco, Guillermo Luppino, Luigi Tommaso Kuttner, Samuel Arrese, Ignacio Solheim, Ole Eikenes, Live Karlberg, Anna Perez-Nunez, Angel Escudero, Trinidad Hornero, Roberto Sarabia, Rosario Image and Video Processing Computer Vision and Pattern Recognition Accurately assessing tumor removal is paramount in the management of glioblastoma. We developed a pipeline using MRI scans and neural networks to segment tumor subregions and the surgical cavity in postoperative images. Our model excels in accurately classifying the extent of resection, offering a valuable tool for clinicians in assessing treatment effectiveness. |
| title | Postoperative glioblastoma segmentation: Development of a fully automated pipeline using deep convolutional neural networks and comparison with currently available models |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.11725 |