深度学习在细胞图像自动分割中的应用与进展OA

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.