摘要
膝关节是类风湿性关节炎(Rheumatoid Arthritis,RA)常见累及关节,膝关节滑膜的精准分割对RA诊断和治疗有重要影响,本文提出了一种基于VNet网络的改进算法对膝关节滑膜磁共振图像进行自动分割.首先对39名滑膜炎患者的膝关节磁共振图像进行数据预处理,通过将Transformer编码器嵌入VNet网络底部的方式构建VNetTrans网络,使用MemSwish激活函数进行训练.最终模型平均Dice系数为0.7585,HD为24.6 mm;相较于VNet,Dice系数提升0.0836,HD距离减少10 mm.实验结果表明,该算法可对膝关节磁共振图像中滑膜增生区域实现较好的3D分割,具有诊断和监测RA发展过程的应用价值.
Knee joint is commonly hurt by rheumatoid arthritis(RA).Accurate segmentation of synovium is essential for the diagnosis and treatment of RA.This paper proposes an algorithm based on improved VNet for automatically segmenting knee joint synovial magnetic resonance images.Firstly,the knee joint magnetic resonance images of 39 patients with synovitis were preprocessed.VNetTrans was constructed by embedding Transformer at the bottom of VNet.The MemSwish activation function was used for training.The average Dice score of the final model is 0.7585 and the HD is 24.6 mm.Compared with VNet,the proposed model increased Dice score by 0.0836 and decreased HD by 10 mm.Experimental results demonstrated that the proposed algorithm achieved satisfying 3D segmentation of the synovial hyperplasia area in the knee magnetic resonance images.It can be utilized to facilitate the diagnosis and monitoring of RA.
作者
王颖珊
邓奥琦
毛瑾玲
朱中旗
石洁
杨光
马伟伟
路青
汪红志
WANG Ying-shan;DENG Ao-qi;MAO Jin-ling;ZHU Zhong-qi;SHI Jie;YANG Guang;MA Wei-wei;LU Qing;WANG Hong-zhi(Shanghai Key Laboratory of Magnetic Resonance,School of Physics and Electronic Science,East China Normal University,Shanghai 200062,China;Shanghai Guanghua Hospital of Integrated Traditional Chinese and Western Medicine,Shanghai 200052,China;College of Acupuncture and Massage,Shanghai University of Chinese Medicine,Shanghai 200032,China;Renji Hospital,School of Medicine,Shanghai Jiao Tong University,Shanghai 200127,China)
出处
《波谱学杂志》
CAS
北大核心
2022年第3期303-315,共13页
Chinese Journal of Magnetic Resonance
基金
国家自然科学基金重点项目(61731009).
关键词
磁共振图像
医学图像分割
深度学习
滑膜炎
magnetic resonance image
medical image segmentation
deep learning
synovitis
