Abstract

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Author(s):: HUANG Qianjia1; ZHANG Heng2; 3; 4; 5; LI Qixuan1; CAO Dezheng2; 3; 4; 5; JIAO Zhuqing1; NI Xinye2; 3; 4; 5; 1. School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China 2. Department of Radiotherapy, Changzhou Second Peoples Hospital, Nanjing Medical University, Changzhou 213003, China 3. Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213003, China 4. Medical Physics Research Center, Nanjing Medical University, Changzhou 213003, China 5. Key Laboratory of Medical Physics in Changzhou, Changzhou 213003, China

Keywords:: Keywords: medical image segmentation deep learning threshold segmentation neural network segment anything model review

Abstract:: Abstract: Medical image is a powerful tool to assist doctors in the diagnosis and treatment planning. Nowadays, the segmentation of medical images is no longer limited to manual segmentation methods. Traditional methods and deep learning methods have been used to achieve more accurate results in medical image segmentation. Herein some innovative medical image segmentation methods in recent years are reviewed. By elaborating on the innovations of deep learning methods (SAM, SegNet, Mask R-CNN, and U-NET) and traditional methods (active contour model and threshold segmentation model), the differences and similarities between them are compared. The summary of medical image segmentation methods and the prospect is expected to help researchers better grasp and familiarize themselves with research status and development trend.