Brain Edema and Tumor Detection Using novel autoencoder u-net architecture

Abstract

Brain tumor and edema localization and segmentation from magnetic resonance imaging (MRI) are hard and important tasks for several applications in the field of medical analysis. As each brain imaging modality gives unique and key details related to each part of the tumor and edema. A tumor could be found in any area of the brain and could be of any size, shape, and contrast. There may exist multiple tumors of different types in a human brain at the same time. Accurate tumor area segmentation is considered primary step for treatment of brain tumors. Deep Learning is a set of promising techniques that could provide better results as compared to nondeep learning techniques for segmenting timorous part inside a brain. In this work, we propose a fully automatic brain tumor and edema segmentation technique in brain magnetic resonance (MR) images. we focus on the recent trend of deep learning methods in this field. First, an introduction to brain tumors and methods for brain tumor and edema segmentation is given. Then, the proposed network uses BRATS segmentation challenge dataset which is composed of images obtained through four different modalities. Accordingly, we present an extended version of existing network to solve segmentation problem. Finally, an assessment of the current state is presented and future developments to standardize MRI-based brain tumor segmentation methods into daily clinical routine are addressed.