Research on medical image segmentation technology based on deep learning

Abstract

With economy expanding quickly, people's demand for medical services has become higher and higher, and medical image as an important basis for medical diagnosis has naturally received widespread attention. However, traditional image segmentation methods are easily affected by noise and unable to meet the complex and changing practical clinical applications. The increasing utilization of deep learning technology enables effective resolution of these problems. In this paper, we will first introduce the traditional image segmentation techniques, and describe the main methods to realize traditional image segmentation and its limitations. Immediately after that, it is proposed that deep learning methods can solve the challenges of medical image segmentation with traditional methods, and then the structure, algorithms and applications of several of the most commonly used deep learning methods are introduced. This paper proposes that medical image segmentation based on deep learning can segment the image more robustly and with high accuracy, and it can automatically obtain the most suitable features. The research in this paper will be of great value to the research and application of medical image segmentation technology based on deep learning.