中图分类号：TP391 文献标志码：A DOI: 10.16157/j.issn.0258-7998.257321 中文引用格式： 程卓. 基于深度学习的医学图像分割算法综述[J]. 电子技术应用，2026，52(9)：86-93. 英文引用格式： Cheng Zhuo. A review of deep learning-based medical image segmentation algorithms[J]. Application of Electronic Technique，2026，52(9)：86-93.
A review of deep learning-based medical image segmentation algorithms
Cheng Zhuo
Faculty of Information Science and Engineering， Ningbo University
Abstract： Medical image segmentation plays a pivotal role in intelligent diagnosis and clinical decision-making, serving as the foundation for disease detection, organ delineation, and intraoperative navigation. In recent years, the rapid development of deep learning has greatly advanced segmentation algorithms, yet challenges remain due to modality heterogeneity, blurred boundaries, and limited annotated data. This paper provides a comprehensive review of deep learning–based medical image segmentation methods, covering discriminative models such as Convolutional Neural Networks, Recurrent Neural Networks, and Transformers, as well as generative approaches including Generative Adversarial Networks and diffusion models. Furthermore, this paper discusses the adaptation strategies of large foundation models, represented by the Segment Anything Model, in medical imaging tasks. In addition, this work summarizes the key challenges faced by current segmentation methods and presents experimental comparisons of representative models across multiple multimodal datasets. Finally, the paper outlines the current research trends and future directions, offering insights for improving segmentation model design and facilitating their clinical applications.
Key words : medical image segmentation；deep learning；generative models；large medical segmentation models
