Image Segmentation of Alzheimer's Disease MRI Images Based on Deep Learning

Abstract

Alzheimer's Disease (AD) is a degenerative disease that affects the central nervous system. With the intensification of population aging in today's society, the number of patients is gradually increasing. As an auxiliary diagnostic method, Magnetic Resonance Imaging (MRI) images can clearly show the changes of specific brain structures, and image segmentation technology is the core method to extract relevant regions from MRI images. This article provides a review of the research on deep learning (DL) in MRI image segmentation for AD. Firstly, the article focuses on analyzing two image segmentation methods, U-Net and V-Net, from both their principles and applications. By comparing the cutting-edge methods of recent research, the advantages and disadvantages of two-dimensional and three-dimensional segmentation methods are discussed and analyzed. The study found that the advantages and disadvantages of the two dimensions exhibit complementary characteristics, where the strengths (weaknesses) of one dimension are the weaknesses (strengths) of another dimension. Finally, the challenges and future trends of current research are discussed, such as introducing or strengthening interpretable algorithms and designing adaptive data processing algorithms, in order to provide a reference for subsequent research.