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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
Conference Name7th 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
Source PublicationIntravascular 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
Volume11043 LNCS
Conference Date2018-09-16
Conference PlaceGranada, Spain
PublisherSpringer Verlag
Contribution Rank1

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.

Indexed ByEI
EI Accession Number20184506032888
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Document Type会议论文
Corresponding AuthorChen, Yundai
Affiliation1.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
Recommended Citation
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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