Review on medical image segmentation methods
10.3969/j.issn.1005-202X.2024.08.003
- VernacularTitle:医学图像分割的研究进展
- Author:
Qianjia HUANG
1
;
Heng ZHANG
;
Qixuan LI
;
Dezheng CAO
;
Zhuqing JIAO
;
Xinye NI
Author Information
1. 常州大学计算机与人工智能学院,江苏常州 213164
- Keywords:
medical image segmentation;
deep learning;
threshold segmentation;
neural network;
segment anything model;
review
- From:
Chinese Journal of Medical Physics
2024;41(8):939-945
- CountryChina
- Language:Chinese
-
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.
