Trends and Techniques in Medical Image Segmentation for Disease Detection

Abstract

Medical images have become an indispensable and important tool for the diagnosis of medical conditions and surgical guidance. As computer vision technology advances, Medical image segmentation technology has effectively assisted clinicians in making accurate diagnoses and providing personalized treatment. In this paper, some excellent medical image segmentation methods in recent years are summarized, and according to the deep learning method (e.g. Convolutional Neural Network (CNN), U- net, etc.), and traditional methods (such as active contour model, threshold segmentation model, etc.) are sorted out. This paper compares various image segmentation methods, analyzes their similarities and differences, and summarizes and looks forward to the future development of medical image segmentation technology. With the continuous advancement of computer vision models, medical image segmentation is expected to become increasingly accurate and efficient. This will significantly enhance the speed and accuracy of medical image processing, helping doctors to better identify and analyze diseases, thereby providing more accurate clinical diagnoses and treatment plans. With these technological advancements, future medical image segmentation will not only handle more complex images but also enable more intelligent and automated analysis, offering strong support for clinical practice.