Segmentation of Brain Tumors Using Three-Dimensional Convolutional Neural Network on MRI Images 3D MedImg-CNN

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 MedImg-CNN) approach which achieves high performance while being extremely efficient, a balance that existing methods have struggled to achieve. Our 3D MedImg-CNN is formed directly on the raw image modalities and thus learn a characteristic representation directly from the data. We propose a new cascaded 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. The performance of the proposed 3D MedImg-CNN with CNN segmentation method is computed using dice similarity coefficient (DSC). In experiments on the 2013, 2015 and 2017 BraTS challenges datasets; we unveil that our approach is among the most powerful methods in the literature, while also being very effective.