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Corners Detection for Bioresorbable Vascular Scaffolds Segmentation in IVOCT Images
Yao, Linlin1,2; Cao, Yihui1,3; Jin, Qinhua4; Jing, Jing4; Chen, Yundai4; Li, Jianan1,3; Zhu, Rui1,3
Conference Name21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Source PublicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
Volume11073 LNCS
Conference Date2018-09-16
Conference PlaceGranada, Spain
PublisherSpringer Verlag
Contribution Rank1

Bioresorbable Vascular scaffold (BVS) is a promising type of stent in percutaneous coronary intervention. Struts apposition assessment is important to ensure the safety of implanted BVS. Currently, BVS struts apposition analysis in 2D IVOCT images still depends on manual delineation of struts, which is labor intensive and time consuming. Automatic struts segmentation is highly desired to simplify and speed up quantitative analysis. However, it is difficult to segment struts accurately based on the contour, due to the influence of fractures inside strut and blood artifacts around strut. In this paper, a novel framework of automatic struts segmentation based on four corners is introduced, in which prior knowledge is utilized that struts have obvious feature of box-shape. Firstly, a cascaded AdaBoost classifier based on enriched haar-like features is trained to detect struts corners. Then, segmentation result can be obtained based on the four detected corners of each strut. Tested on the same five pullbacks consisting of 480 images with strut, our novel method achieved an average Dice’s coefficient of 0.85 for strut segmentation areas, which is increased by about 0.01 compared to the state-of-the-art. It concludes that our method can segment struts accurately and robustly and has better performance than the state-of-the-art. Furthermore, automatic struts malapposition analysis in clinical practice is feasible based on the segmentation results. © 2018, Springer Nature Switzerland AG.

KeywordIntravascular optical coherence tomography Bioresorbable Vascular Scaffolds segmentation Corners detection
Indexed ByEI ; CPCI
WOS IDWOS:000477769100063
EI Accession Number20183905872897
Citation statistics
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.University of Chinese Academy of Sciences, Beijing, China;
3.Shenzhen Vivolight Medical Device & Technology Co., Ltd., Shenzhen, China;
4.Department of Cardiology, Chinese PLA General Hospital, Beijing, China
Recommended Citation
GB/T 7714
Yao, Linlin,Cao, Yihui,Jin, Qinhua,et al. Corners Detection for Bioresorbable Vascular Scaffolds Segmentation in IVOCT Images[C]:Springer Verlag,2018:552-560.
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