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Deep Learning-Based Detection and Segmentation for BVS Struts in IVOCT Images

  • Yihui Cao
  • Yifeng Lu
  • Qinhua Jin
  • Jing Jing
  • Yundai ChenEmail author
  • Jianan Li
  • Rui Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11043)

Abstract

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.

Keywords

Bioresorbable vascular scaffold Intravascular optical coherence tomography Detection and segmentation Deep learning 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yihui Cao
    • 1
    • 2
  • Yifeng Lu
    • 1
    • 3
  • Qinhua Jin
    • 4
  • Jing Jing
    • 4
  • Yundai Chen
    • 4
    Email author
  • Jianan Li
    • 1
    • 2
  • Rui Zhu
    • 1
    • 2
  1. 1.State Key Laboratory of Transient Optics and Photonics Xi’an Institute of Optics and Precision MechanicsChinese Academy of SciencesXi’anPeople’s Republic of China
  2. 2.Shenzhen Vivolight Medical Device & Technology Co., Ltd.ShenzhenPeople’s Republic of China
  3. 3.University of Chinese Academy of SciencesBeijingPeople’s Republic of China
  4. 4.Department of CardiologyChinese PLA General HospitalBeijingPeople’s Republic of China

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