Deep Learning for Brain MRI Segmentation

Abstract

Brain magnetic resonance imaging (MRI) plays a central role in this setting, providing detailed anatomical information for clinical evaluation. Segmentation of her MRI images of the brain is an essential step to obtaining meaningful information for diagnostic and therapeutic purposes. Deep learning, especially convolutional neural networks (CNN), has emerged as a powerful solution to automate brain MRI segmentation and improve accuracy. In particular, convolutional neural networks have demonstrated remarkable performance in segmenting brain structures such as gray matter, white matter, and cerebrospinal fluid, as well as detecting abnormalities such as tumors and lesions. The purpose of this article is to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First, we review current deeplearning architectures used to segment anatomical brain structures and lesions. Next, we summarize and discuss the performance, speed, and characteristics of deep learning approaches. Finally, it critically assesses the current situation and points out possible future developments and trends. Keywords—Deep Learning, Convolutional Neural Networks, Brain MRI.