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Deep learning-based detection and segmentation for bvs struts in IVOCT images
Cao, Yihui1,2; Lu, Yifeng1,3; Jin, Qinhua4; Jing, Jing4; Chen, Yundai4; Li, Jianan1,2; Zhu, Rui1,2
2018
会议名称7th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2018, and the 3rd International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2018, held in conjunction with the 21th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018
会议录名称Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis - 7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018 Held in Conjunction with MICCAI 2018
卷号11043 LNCS
页码55-63
会议日期2018-09-16
会议地点Granada, Spain
出版者Springer Verlag
产权排序1
摘要

Bioresorbable Vascular Scaffold (BVS) is the latest stent type for the treatment of coronary artery disease. A major challenge of BVS is that once it is malapposed during implantation, it may potentially increase the risks of late stent thrombosis. Therefore it is important to analyze struts malapposition during implantation. This paper presents an automatic method for BVS malapposition analysis in intravascular optical coherence tomography images. Struts are firstly detected by a detector trained through deep learning. Then, struts boundaries are segmented using dynamic programming. Based on the segmentation, apposed and malapposed struts are discriminated automatically. Experimental results show that the proposed method successfully detected 97.7% of 4029 BVS struts with 2.41% false positives. The average Dice coefficient between the segmented struts and ground truth was 0.809. It concludes that the proposed method is accurate and efficient for BVS struts detection and segmentation, and enables automatic malapposition analysis. © Springer Nature Switzerland AG 2018.

作者部门瞬态光学技术国家重点实验室
DOI10.1007/978-3-030-01364-6_7
收录类别EI
ISBN号9783030013639
语种英语
ISSN号03029743;16113349
EI入藏号20184506032888
引用统计
文献类型会议论文
条目标识符http://ir.opt.ac.cn/handle/181661/30715
专题瞬态光学技术国家重点实验室
通讯作者Chen, Yundai
作者单位1.State Key Laboratory of Transient Optics and Photonics Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, China;
2.Shenzhen Vivolight Medical Device & Technology Co., Ltd., Shenzhen, China;
3.University of Chinese Academy of Sciences, Beijing, China;
4.Department of Cardiology, Chinese PLA General Hospital, Beijing, China
推荐引用方式
GB/T 7714
Cao, Yihui,Lu, Yifeng,Jin, Qinhua,et al. Deep learning-based detection and segmentation for bvs struts in IVOCT images[C]:Springer Verlag,2018:55-63.
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