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Alternative TitleAutomatic Lumbar Vertebrae Recognition in Intraoperative X-Ray Images Based on Hierarchical Recurrent Neural Network
李杨1,2,3; 梁炜1,3; 张吟龙1,2,3; 安海博1,2,3; 谈金东4
Source Publication计算机辅助设计与图形学学报
Indexed ByEI ; CSCD
EI Accession number20191206655091
Contribution Rank1
Funding Organization国家自然科学基金重点项目(61333019) ; 中国科学院国际伙伴计划(173321KYSB20180020)
Keyword图像识别 循环神经网络 曲率特征 图像引导手术 移动C型臂
Other AbstractAccording to the characteristic of mobile C-arm X-ray imaging in image-guided minimally invasive spine surgery, an automatic lumbar vertebrae recognition method is proposed, which based on hierarchical recurrent neural network. Its purpose is to identify lumbar vertebrae automatically by learning the curvature features. First, in order to solve the problem of lumbar vertebrae texture overlapping in X-ray images, the curvature features of 3D lumbar vertebrae model, which are common to the 2D X-ray images, are taken as the input of the model. Second, in order to simulate the multi-view imaging of intraoperative C-arm, the bidirectional recurrent neural network is exploited to learn the correlation of lumbar curvature features at different imaging angles. Finally, in order to solve the problem of partial occlusion of the lumbar vertebrae in the pathological condition, a hierarchical recurrent neural network model is introduced. The natural context between human lumbar vertebrae is modeled by the layer-by-layer fusion architecture to improve the recognition rate in the pathological condition. The results of the verification on open source datasets and intraoperative mobile C-arm X-ray images show that the lumbar vertebrae recognition rate of the proposed method is superior to the other four methods in both normal and pathological conditions. Furthermore, due to the utilization of two-dimensional curvature features, the proposed method is more efficient in the training and testing phases, and more suitable for applications in intraoperative image-guided navigation.
Citation statistics
Cited Times:1[CSCD]   [CSCD Record]
Document Type期刊论文
Corresponding Author梁炜
4.Department of Mechanical Aerospace and Biomedical Engineering University of Tennessee
Recommended Citation
GB/T 7714
李杨,梁炜,张吟龙,等. 基于层级循环神经网络的术中X线图像腰椎自动识别[J]. 计算机辅助设计与图形学学报,2019,31(1):132-140.
APA 李杨,梁炜,张吟龙,安海博,&谈金东.(2019).基于层级循环神经网络的术中X线图像腰椎自动识别.计算机辅助设计与图形学学报,31(1),132-140.
MLA 李杨,et al."基于层级循环神经网络的术中X线图像腰椎自动识别".计算机辅助设计与图形学学报 31.1(2019):132-140.
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