Application of Image Segmentation Technology Based on Machine Learning in Medical Image Analysis

Beijing Normal University, Hong Kong Baptist University United International College, Faculty of Science And Technology, 519000 Zhuhai, China

^* Corresponding author: s230006267@mail.uic.edu.cn

Abstract

Medical image analysis heavily relies on the crucial step of image segmentation, which possesses the capability to discern and differentiate various structures within medical imagery. Furthermore, this process holds extensive applicability and research significance across numerous medical domains, encompassing clinical diagnosis, pathological evaluations, surgical planning, and more. With the rapid advancement of machine learning methodologies, especially deep learning, the realm of medical image segmentation has undergone substantial enhancements. This paper delves into machine learning-driven image segmentation techniques, emphasizing the implementation of deep learning principles within medical image analysis. It further examines the significance and evolution of these technologies. Additionally, the article discusses the profound impacts of these technologies on disease diagnosis and clinical practice, particularly in enhancing diagnostic precision and treatment efficacy. Specifically, this paper highlights the pivotal role of models like U-Net, V-Net, and 3D U-Net in elevating the accuracy of medical image segmentation. These models have contributed significantly to the progression of medical imaging technology and have been instrumental in diagnosing and treating various diseases. By comparing the utilization and influence of these models, this paper intends to offer insightful references and guidance to researchers and clinicians in the domain of medical image segmentation.