Image Segmentation for Medical Analysis
Tao Lei, Asoke Kumar Nandi · 2022
This chapter explains image segmentation in medical analysis. It deals with an introduction and presents recent work related to medical image segmentation. The chapter introduces two representative medical image segmentation methods. Medical image segmentation aims to make anatomical or pathological structure changes clearer in images; it often plays a key role in computer-aided diagnosis and smart medicine due to the great improvement in diagnostic efficiency and accuracy. In order to help clinicians make accurate diagnoses, it is necessary to segment some key objects in medical images and extract features from the segmented regions. Although deep learning can achieve better end-to-end liver segmentation, it causes some new problems that limit the clinical deployment of deep learning. The chapter presents a lightweight 3D network based on V-Net. Although the networks can perform end-to-end liver and livertumor segmentation well, the use of vanilla convolution limits further improvements of segmentation accuracy.