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基于层级循环神经网络的术中X线图像腰椎自动识别
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
Department工业控制网络与系统研究室
Source Publication计算机辅助设计与图形学学报
ISSN1003-9775
2019
Volume31Issue:1Pages:132-140
Indexed ByEI ; CSCD
EI Accession number20191206655091
CSCD IDCSCD:6410613
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
Abstract针对图像引导微创脊柱手术中移动C型臂X线成像特点,通过学习人体腰椎的曲率特征实现腰椎识别,提出一种基于层级循环神经网络的X线图像腰椎自动识别方法.首先为解决X线图像中腰椎纹理混叠的问题,提取腰椎三维模型与二维X线图像中共有的曲率特征作为模型的输入;其次为模拟术中移动C型臂多角度成像的特点,采用双向循环神经网络学习腰椎曲率特征,刻画腰椎曲率特征在不同成像角度下的关联性;最后为解决病理情况下腰椎部分信息缺失的问题,提出一种层级循环神经网络模型,通过逐层融合的网络架构对人体腰椎间天然的上下文关系进行建模,提高模型在病理情况下的腰椎识别率.在开源数据集和术中移动C型臂X线图像上的实验结果表明,文中方法在正常情况和病理情况下的腰椎识别率均优于其他4种方法,且由于使用了数据量较少的二维曲率特征,该方法在训练和测试阶段的计算效率更高,更适合于术中图像引导的应用.
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.
Language中文
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/24105
Collection工业控制网络与系统研究室
Corresponding Author梁炜
Affiliation1.中国科学院沈阳自动化研究所工业控制网络与系统研究室
2.中国科学院机器人与智能制造创新研究院
3.中国科学院大学
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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