基于3D VNetTrans的膝关节滑膜磁共振图像自动分割
收稿日期: 2022-03-23
网络出版日期: 2022-05-11
基金资助
国家自然科学基金重点项目(61731009)
Automatic Segmentation of Knee Joint Synovial Magnetic Resonance Images Based on 3D VNetTrans
Received date: 2022-03-23
Online published: 2022-05-11
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0.758 5，HD为24.6 mm；相较于VNet，Dice系数提升0.083 6，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．
王颖珊 , 邓奥琦 , 毛瑾玲 , 朱中旗 , 石洁 , 杨光 , 马伟伟 , 路青 , 汪红志 . 基于3D VNetTrans的膝关节滑膜磁共振图像自动分割[J]. 波谱学杂志, 2022 , 39(3) : 303 -315 . DOI: 10.11938/cjmr20222988
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 0.758 5 and the HD is 24.6 mm. Compared with VNet, the proposed model increased Dice score by 0.083 6 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.
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