A deep learning architecture for brain tumor segmentation in MRI images

Abstract

With the advent of new technologies in the field of medicine, there is rising awareness of biomechanisms, and we are better able to treat ailments than we could earlier. Deep learning has helped a lot in this endeavor. This paper deals with the application of deep learning in brain tumor segmentation. Brain tumors are difficult to segment automatically given the high variability in the shapes and sizes. We propose a novel yet simple fully convolutional network (FCN) which results in competitive performance and faster runtime than state-of-the-art model. Using the database provided for the Brain Tumor Segmentation (BraTS) challenge by the Medical Image Computing and Computer Assisted Intervention (MICCAI) society, we are able to achieve dice scores of 0.83 in the whole tumor region, 0.75 in the core tumor region and 0.72 in the enhancing tumor region, while our method is about 18 times faster than the state-of-the-art.