Journals Articles Conferences News About CLP
Submit a paper Email Alert
Search
Search
Articles
Journals
News
Advanced Search
Top Searches
2D MaterialsTransformation opticsQuantum PhotonicsSmall lasersSemiconductor UV PhotonicsSupersymmetric microring laser arrays

Acta Optica Sinica, Volume. 39, Issue 7, 0715001(2019)

Corner Detection-Based Segmentation Algorithm of Bioresorbable Vascular Scaffold Strut Contours

Linlin Yao1,2、**, Qinhua Jin3, Jing Jing3, Yundai Chen3, Yihui Cao1, Jianan Li1, and Rui Zhu1、*
Author Affiliations
  • 1 State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi 710119, China
  • 2 University of Chinese Academy of Sciences, Beijing 100049, China
  • 3 Department of Cardiology, Chinese PLA General Hospital, Beijing 100853, China
    • show less
    AbstractGet PDF(in Chinese)
    Figures&Tables (8)
    Equations (0)
    References (23)
    Cited By (1)
    Tools
    Paper Information
    Topics
    Figures & Tables(8)
    IVOCT images after BVS stenting in Cartesian coordinate system and process where strut contour can be represented from four labeled corners by expert. (a) Single enlarged strut; (b) four corners labeled on single strut by expert; (c) strut contour obtained from four labeled corners; (d) IVOCT image after BVS stenting in Cartesian coordinate system

    Fig. 1. IVOCT images after BVS stenting in Cartesian coordinate system and process where strut contour can be represented from four labeled corners by expert. (a) Single enlarged strut; (b) four corners labeled on single strut by expert; (c) strut contour obtained from four labeled corners; (d) IVOCT image after BVS stenting in Cartesian coordinate system

    Download full size
    IVOCT images shown in polar coordinates based on two different transformed centers. (a) IVOCT image in polar coordinate system using center of image in Cartesian coordinate system as transformed center; (b) IVOCT image in polar coordinate system using lumen-contour center of image in Cartesian coordinate system as transformed center

    Fig. 2. IVOCT images shown in polar coordinates based on two different transformed centers. (a) IVOCT image in polar coordinate system using center of image in Cartesian coordinate system as transformed center; (b) IVOCT image in polar coordinate system using lumen-contour center of image in Cartesian coordinate system as transformed center

    Download full size
    Selection of positive and negative training samples

    Fig. 3. Selection of positive and negative training samples

    Download full size
    Basic prototypes with Haar-like features and extended prototypes combined with struts' features. (a)-(e) Basic prototypes with Haar-like features; (f)-(j) extended prototypes combined with struts' features

    Fig. 4. Basic prototypes with Haar-like features and extended prototypes combined with struts' features. (a)-(e) Basic prototypes with Haar-like features; (f)-(j) extended prototypes combined with struts' features

    Download full size
    Flow charts of four corners detection of struts. (a)-(d) Flow chart of four corners detection by cross-division method; (e)-(h) flow chart of four corners detection by strut-angle-division method; (i) method of getting strut angle

    Fig. 5. Flow charts of four corners detection of struts. (a)-(d) Flow chart of four corners detection by cross-division method; (e)-(h) flow chart of four corners detection by strut-angle-division method; (i) method of getting strut angle

    Download full size
    Comparison of strut segmentation results. (a)-(d) Results of BVS strut segmentation using DP algorithm; (e)-(h) results of BVS strut segmentation using proposed algorithms

    Fig. 6. Comparison of strut segmentation results. (a)-(d) Results of BVS strut segmentation using DP algorithm; (e)-(h) results of BVS strut segmentation using proposed algorithms

    Download full size
    3D reconstruction models of BVS struts. (a) 3D reconstruction model of BVS strut with coronary artery; (b) 3D reconstruction model of BVS strut without coronary artery

    Fig. 7. 3D reconstruction models of BVS struts. (a) 3D reconstruction model of BVS strut with coronary artery; (b) 3D reconstruction model of BVS strut without coronary artery

    Download full size

    • Table 1. Results of strut detection and segmentation

      View table

      Table 1. Results of strut detection and segmentation

      Data setNo. FNo. GTEcp /μmSegmentation(Dice)
      Method in Ref.[10]Proposed
      No.18169124.77±15.080.800.90
      No.211992827.88±20.390.790.89
      No.3118117231.45±20.480.800.87
      No.47860425.27±18.450.820.89
      No.5147118830.68±19.840.820.88
      No.68663529.80±19.540.800.89
      No.77660327.84±19.360.790.89
      No.8150124030.23±19.960.800.87
      Average--28.99±19.390.800.88
      No. F: number of frames evaluated; No. GT: number of ground truth
    Tools

    Get Citation

    Copy Citation Text

    Linlin Yao, Qinhua Jin, Jing Jing, Yundai Chen, Yihui Cao, Jianan Li, Rui Zhu. Corner Detection-Based Segmentation Algorithm of Bioresorbable Vascular Scaffold Strut Contours[J]. Acta Optica Sinica, 2019, 39(7): 0715001

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Set citation alerts for article
    Save article for my favorites
    Paper Information

    Category: Machine Vision

    Received: Jan. 15, 2019

    Accepted: Mar. 21, 2019

    Published Online: Jul. 16, 2019

    The Author Email: Yao Linlin (yaolinlin16@mails.ucas.ac.cn), Zhu Rui (rzhu@vivo?light.com)

    DOI:10.3788/AOS201939.0715001

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology