计算机工程与应用2025，Vol.61Issue(20)：54-74,21.DOI:10.3778/j.issn.1002-8331.2412-0272
Transformer在医学图像分割中的研究进展
Research Progress of Transformers in Medical Image Segmentation
摘要
Abstract
With the growing demand for high-precision diagnostics in society,automated medical image segmentation technologies have assumed a pivotal role in modern medical practice.Although convolutional neural network(CNN)has demonstrated excellent performance in medical image segmentation,its inherent limitations have prompted many re-searchers to incorporate Transformers into this domain to address CNN's shortcomings in global contextual learning.This paper first reviews the structure of Transformers and their variants,analyzing their integration and application in medical image segmentation tasks.Focusing on five major segmentation tasks:cardiac,brain,lung,abdominal and other regions,it summarizes the research progress combining U-Net and other models,highlighting their advantages in capturing multi-scale features,improving segmentation accuracy,and addressing the complexity of diverse anatomical structures.Addi-tionally,existing studies are discussed,emphasizing the need for further in-depth research to advance the development of medical image segmentation technologies.关键词
深度学习/Transformer/医学图像分割/卷积神经网络(CNN)Key words
deep learning/Transformer/medical image segmentation/convolutional neural network(CNN)分类
信息技术与安全科学引用本文复制引用
周振霄,王华,魏德健,曹慧,姜良,王锡城..Transformer在医学图像分割中的研究进展[J].计算机工程与应用,2025,61(20):54-74,21.基金项目
国家自然科学基金面上项目(82374620) （82374620）
山东省自然科学基金面上项目(ZR2024MH193). （ZR2024MH193）
