SIA OpenIR  > 工业控制网络与系统研究室
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)
Department工业控制网络与系统研究室
Conference Name2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)
Conference DateAugest 16-20, 2016
Conference PlaceOrlando, FL, USA
Source Publication2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)
PublisherIEEE
Publication PlaceNew York
2016
Pages647-650
Indexed ByEI ; CPCI(ISTP)
EI Accession number20170303248180
WOS IDWOS:000399823501008
Contribution Rank1
ISSN1558-4615
ISBN978-1-4577-0219-8
AbstractAutomatic 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.
Language英语
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/19509
Collection工业控制网络与系统研究室
Corresponding AuthorLiang W(梁炜); Tan JD(谈金东)
Affiliation1.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
Recommended Citation
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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