| Revue: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560559 |
| ISSN: | 1405-5546 |
| Autores: | Kharrat, Ahmed1 Neji, Mahmoud2 |
| Instituciones: | 1University of Sfax, MIRACL Laboratory ISIMS, Sakiet Ezzeit, Sfax. Túnez 2University of Sfax, MIRACL Laboratory FSEG, Elmatar, Sfax. Túnez |
| Año: | 2020 |
| Periodo: | Oct-Dic |
| Volumen: | 24 |
| Número: | 4 |
| Paginación: | 1617-1626 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | We consider the problem of fully automatic brain tumor segmentation in MR images containing glioblastomas. We propose a three Dimensional Convolutional Neural Network (3D-CNN) approach that achieves high performance while being extremely efficient, a balance that existing methods have struggled to achieve. Our 3D-Brain CNN is formed directly on raw image modalities and thus learn a characteristic representation directly from the data. We propose a new cascading architecture with two pathways that each model normal details in tumors. Fully exploiting the convolutional nature of our model also allows us to segment a complete cerebral image in one minute. In experiments on the 2013 and 2015 BRATS challenge dataset; we exhibit that our approach is among the most powerful methods in the literature, while also being very effective. |
| Keyword: | Brain tumor, Segmentation, Deep learning, Convolutional neural networks |
| Texte intégral: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
