计算机工程2026，Vol.52Issue(5)：81-94,14.DOI:10.19678/j.issn.1000-3428.0253035
面向医学图像分割的深度学习模型架构与性能评估方法综述
Review on Deep Learning Model Architectures and Performance Evaluation Methods for Medical Image Segmentation
摘要
Abstract
Medical image segmentation enables pixel-level localization of lesions or anatomical structures in multimodal imaging data and serves as a key foundation for computer-aided diagnosis and clinical decision-making.This study addresses the rapid evolution of medical image segmentation network architectures and the inherent limitations(semantic ambiguity and statistical instability)of existing evaluation metrics.This study aims to systematically examine and delineate the alignment among network structure,task characteristics,and evaluation metrics;reveal the method development path and performance boundaries;and establish a structure-metric matching mechanism tailored to practical clinical needs.Based on representative literature from the Web of Science Core Collection between 2020 and 2025,this study first reviews the design mechanisms and evolutionary pathways of core architectures,such as Transformers,Graph Neural Networks(GNNs),and Diffusion Models(DMs),and then summarizes the essential characteristics of lightweight,hybrid,and prompt-guided paradigms.Subsequently,by integrating empirical studies on public datasets,a quantitative comparison is conducted across different architectures in typical segmentation tasks involving organs,tumors,and brain tissues,covering common metrics such as the Dice Similarity Coefficient(DSC),95％Hausdorff Distance(HD95),and Intersection over Union(IoU).The results indicate that HD95 exhibits high variability in boundary-complex tasks,DSC shows limited sensitivity to small targets,and IoU presents insufficient structural discrimination capability.Furthermore,this study reveals the statistical causes underlying metric misapplication and task-metric mismatch;constructs a task-structure-to-metric recommendation mapping;proposes a task-granularity-based metric selection strategy;and explores how dynamic networks,self-supervised learning,and cross-modal modeling contribute to the enhancement of model generalization.关键词
医学图像分割/深度学习/网络架构/评价指标体系/任务适配Key words
medical image segmentation/deep learning/network architecture/evaluation metric system/task adaptation分类
信息技术与安全科学引用本文复制引用
李辉,刘佳煜,徐雅萍..面向医学图像分割的深度学习模型架构与性能评估方法综述[J].计算机工程,2026,52(5):81-94,14.基金项目
中央高水平医院临床科研业务费专项资金(2025NHLHCRFHLA04). （2025NHLHCRFHLA04）
