A System for Brain Image Segmentation and Classification Based on Three-Dimensional Convolutional Neural Network
Document title: A System for Brain Image Segmentation and Classification Based on Three-Dimensional Convolutional Neural Network
Journal: Computación y sistemas
Database:
System number: 000560559
ISSN: 1405-5546
Authors: 1
2
Institutions: 1University of Sfax, MIRACL Laboratory ISIMS, Sakiet Ezzeit, Sfax. Túnez
2University of Sfax, MIRACL Laboratory FSEG, Elmatar, Sfax. Túnez
Year:
Season: Oct-Dic
Volumen: 24
Number: 4
Pages: 1617-1626
Country: México
Language: Inglés
English abstract 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
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