计算机工程与应用2026，Vol.62Issue(2)：73-91,19.DOI:10.3778/j.issn.1002-8331.2502-0018
深度学习在细胞图像自动分割中的应用与进展
Advances in Deep Learning for Automated Cell Image Segmentation
摘要
Abstract
Cell segmentation is of great significance for cell morphology analysis,early disease diagnosis,drug screening,and personalized medicine.As a fundamental task,it extracts cell boundaries and structures from biological complex images,thereby advancing disease diagnosis and biomedical research.Accurate cell segmentation is therefore of great importance for tackling challenges in cell morphology analysis,tumor detection,and drug screening.In recent years,deep learning has become a key technology in automated cell image segmentation due to its powerful feature extraction and adaptive learning capabilities.To advance research on cell image segmentation,this paper reviews common performance evalua-tion metrics for cell image segmentation and systematically analyzes the applications of CNN,U-Net,Mask R-CNN,GAN,Transformers,GNN,weakly supervised learning,transfer learning,visual foundation models,and hybrid architec-tures in cell image segmentation.By comparing the strengths and limitations of these models,this study highlights the key challenges in current research and offers insights into future directions.关键词
细胞分割/深度学习/Transformer/弱监督学习/混合架构Key words
cell segmentation/deep learning/Transformer/weakly supervised learning/hybrid architecture分类
信息技术与安全科学引用本文复制引用
王旭,王晓燕,郭英慧,蔡肖红,刘艳艳,张文凯..深度学习在细胞图像自动分割中的应用与进展[J].计算机工程与应用,2026,62(2):73-91,19.基金项目
国家自然科学基金(82074293) （82074293）
山东省研究生优质教育教学资源项目(2024k147) （2024k147）
山东省研究生教育教学改革研究项目(2024G137) （2024G137）
山东中医药大学科学研究基金(KYZK2024M13). （KYZK2024M13）
