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Bi-directional LSTM recurrent neural network for lumbar vertebrae identification in X-ray images
Li Y(李杨)1,2; Liang W(梁炜)1; Tan JD(谈金东)3
作者部门工业控制网络与系统研究室
会议名称2017 International Conference on Computer Systems, Electronics and Control, ICCSEC 2017
会议日期December 25-27, 2017
会议地点Dalian, China
会议录名称2017 International Conference on Computer Systems, Electronics and Control, ICCSEC 2017
出版者IEEE
出版地New York
2017
页码1047-1051
收录类别EI
EI收录号20183905878055
产权排序1
ISBN号978-1-5386-3573-5
关键词image-guided surgery vertebrae identification long short-term memory recurrent neural network curvature feature
摘要Duo to the capability of providing online patient pose, mobile C-arm X-ray images play a key role in image-guided minimally invasive spine surgery. However, automatic lumbar vertebrae identification is still a challenge task because of the inherent limitation of mobile C-arm. In order to solve these problems, a novel automatic lumbar vertebrae identification method is proposed, which based on bidirectional long short-term memory (LSTM) recurrent neural network (RNN). 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 bi-directional recurrent neural network is exploited to learn the correlation of lumbar curvature features at different imaging angles. Finally, in order to avoid of gradient vanishing and error blowing up, the LSTM neuron is applied to replace the notes of bi-directional RNN. Experiment results show that our method identified lumbar vertebrae more accurately than another two methods.
语种英语
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/23362
专题工业控制网络与系统研究室
通讯作者Liang W(梁炜); Tan JD(谈金东)
作者单位1.Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.University of Chinese, Academy of Sciences, Beijing 100049, China
3.Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, TN 37996, United States
推荐引用方式
GB/T 7714
Li Y,Liang W,Tan JD. Bi-directional LSTM recurrent neural network for lumbar vertebrae identification in X-ray images[C]. New York:IEEE,2017:1047-1051.
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