现代信息科技2026，Vol.10Issue(16)：157-162,6.DOI:10.19850/j.cnki.2096-4706.2026.16.029
基于深度学习的膝关节前交叉韧带MRI图像损伤的分割模型研究
Research on a Deep Learning-Based Segmentation Model for MRI Images of Anterior Cruciate Ligament Injuries in the Knee
摘要
Abstract
Accurate imaging evaluation of Anterior Cruciate Ligament(ACL)injuries of the knee joint is crucial for clinical diagnosis and treatment.Due to the slender anatomical structure of the ACL,its small volume,and complex surrounding tissues,traditional Deep Learning models face difficulties in balancing global dependencies with local details.This study proposes an automatic segmentation model based on SwinUNet-FPN.The model uses Swin-UNet with a pure Transformer architecture as the backbone to extract global features through a shifted window self-attention mechanism and introduces a Feature Pyramid Network(FPN)module to fuse deep semantic information with shallow details through lateral connections.Experimental results on the KneeMRI dataset show that the model achieves Dice Similarity Coefficient(DSC)and 95%Hausdorff Distance(HD95)of 0.812 7 and 18.234 5,respectively,significantly outperforming U-Net,ResUNet,and the original Swin-UNet models.The results confirm that this method can effectively overcome segmentation challenges in complex backgrounds,improve edge recognition accuracy,and provide important clinical auxiliary value.关键词
前交叉韧带/MRI图像分割/SwinUnet/特征金字塔网络(FPN)/深度学习Key words
anterior cruciate ligament/MRI image segmentation/Swin-UNet/Feature Pyramid Network(FPN)/Deep Learning分类
信息技术与安全科学引用本文复制引用
陆振展,任文宁,何慧敏..基于深度学习的膝关节前交叉韧带MRI图像损伤的分割模型研究[J].现代信息科技,2026,10(16):157-162,6.基金项目
广西高等教育本科教学改革工程项目(2023JGZ115 （）
广西重点研发计划项目(桂科AB25069086) （桂科AB25069086）
