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Automatic Lumbar Vertebrae Detection Based on Feature Fusion Deep Learning for Partial Occluded C-arm X-ray Images
Li Y(李杨); Liang W(梁炜); Zhang YL(张吟龙); An HB(安海波); Tan JD(谈金东)
作者部门工业控制网络与系统研究室
会议名称2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)
会议日期Augest 16-20, 2016
会议地点Orlando, FL, USA
会议录名称2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)
出版者IEEE
出版地New York
2016
页码647-650
收录类别EI ; CPCI(ISTP)
EI收录号20170303248180
WOS记录号WOS:000399823501008
产权排序1
ISSN号1558-4615
ISBN号978-1-4577-0219-8
摘要Automatic and accurate lumbar vertebrae detection is an essential step of image-guided minimally invasive spine surgery (IG-MISS). However, traditional methods still require human intervention due to the similarity of vertebrae, abnormal pathological conditions and uncertain imaging angle. In this paper, we present a novel convolutional neural network (CNN) model to automatically detect lumbar vertebrae for C-arm X-ray images. Training data is augmented by DRR and automatic segmentation of ROI is able to reduce the computational complexity. Furthermore, a feature fusion deep learning (FFDL) model is introduced to combine two types of features of lumbar vertebrae X-ray images, which uses sobel kernel and Gabor kernel to obtain the contour and texture of lumbar vertebrae, respectively. Comprehensive qualitative and quantitative experiments demonstrate that our proposed model performs more accurate in abnormal cases with pathologies and surgical implants in multi-angle views.
语种英语
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文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/19509
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.Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Konxville, TN, 37996, United States
推荐引用方式
GB/T 7714
Li Y,Liang W,Zhang YL,et al. Automatic Lumbar Vertebrae Detection Based on Feature Fusion Deep Learning for Partial Occluded C-arm X-ray Images[C]. New York:IEEE,2016:647-650.
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