Medical Image Segmentation Research Status and Development Trends

Abstract

As one of the important steps in medical image processing, medical image segmentation plays a pivotal role in clinical surgery and is widely used in application scenarios such as preoperative diagnosis, intraoperative navigation, and postoperative evaluation. In this paper, medical image segmentation technology is studied, and a variety of medical image segmentation methods are categorized and compared in an attempt to explore the development law of medical image segmentation technology. Firstly, the medical image segmentation technology is classified and studied according to its different methods, and this paper mainly researches and organizes the deep learning method for medical image segmentation; secondly, the principle, advantages and disadvantages, and applicable scenarios of each model are analyzed; and lastly, the latest progress of the medical image segmentation technology is objectively described. inherent deficiencies and solutions of the existing techniques, and provides a direction for continued improvement in the future. As one of the important steps in medical image processing, medical image segmentation plays a pivotal role in clinical surgery and is widely used in application scenarios such as preoperative diagnosis, intraoperative navigation, and postoperative evaluation. In this paper, we explore the critical role of medical image segmentation in medical image processing, particularly in clinical surgery and its various applications such as preoperative diagnosis, intraoperative navigation, and postoperative evaluation. Our study focuses on categorizing and comparing different medical image segmentation methods, with a special emphasis on deep learning techniques. We delve into the principles, advantages, disadvantages, and suitable scenarios for each model. Additionally, we objectively present the latest progress in medical image segmentation technology, addressing existing deficiencies and proposing potential solutions. This research provides valuable insights to foster continuous advancements in the field.