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Deep Local-Global Refinement Network for Stent Analysis in IVOCT Images

  • Yuyu Guo
  • Lei Bi
  • Ashnil Kumar
  • Yue Gao
  • Ruiyan Zhang
  • Dagan Feng
  • Qian WangEmail author
  • Jinman Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11768)

Abstract

Implantation of stents into coronary arteries is a common treatment option for patients with cardiovascular disease. Assessment of safety and efficacy of the stent implantation occurs via manual visual inspection of the neointimal coverage from intravascular optical coherence tomography (IVOCT) images. However, such manual assessment requires the detection of thousands of strut points within the stent. This is a challenging, tedious, and time-consuming task because the strut points usually appear as small, irregular shaped objects with inhomogeneous textures, and are often occluded by shadows, artifacts, and vessel walls. Conventional methods based on textures, edge detection, or simple classifiers for automated detection of strut points in IVOCT images have low recall and precision as they are, unable to adequately represent the visual features of the strut point for detection. In this study, we propose a local-global refinement network to integrate local-patch content with global content for strut points detection from IVOCT images. Our method densely detects the potential strut points in local image patches and then refines them according to global appearance constraints to reduce false positives. Our experimental results on a clinical dataset of 7,000 IVOCT images demonstrated that our method outperformed the state-of-the-art methods with a recall of 0.92 and precision of 0.91 for strut points detection.

Keywords

Convolutional neural network (CNN) Intravascular optical coherence tomography (IVOCT) Stent analysis 

Notes

Acknowledgement

This work was supported in part by Australia Research Council (ARC) grants (LP140100686 and IC170100022), the University of Sydney – Shanghai Jiao Tong University Joint Research Alliance (USYD-SJTU JRA) grants and STCSM grant (17411953300).

References

  1. 1.
    Ciompi, F., et al.: Computer-aided detection of intracoronary stent in intravascular ultrasound sequences. Med. Phys. 43(10), 5616–5625 (2016)CrossRefGoogle Scholar
  2. 2.
    Jonathan, L., et al.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015)Google Scholar
  3. 3.
    Kostas, M., et al.: Automatic quantitative analysis of in-stent restenosis using FD-OCT in vivo intra-arterial imaging. Med. Phys. 40(6PartI), 063101 (2013)Google Scholar
  4. 4.
    Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)CrossRefGoogle Scholar
  5. 5.
    Lu, H., et al.: Automatic stent detection in intravascular OCT images using bagged decision trees. Biomed. Opt. Express 3(11), 2809–2824 (2012)CrossRefGoogle Scholar
  6. 6.
    Merget, D., et al.: Robust facial landmark detection via a fully-convolutional local-global context network. In: CVPR, pp. 781–790 (2018)Google Scholar
  7. 7.
    Nam, H.S., et al.: Automated detection of vessel lumen and stent struts in intravascular optical coherence tomography to evaluate stent apposition and neointimal coverage. Med. Phys. 43(4), 1662–1675 (2016)CrossRefGoogle Scholar
  8. 8.
    Otsuka, F., et al.: Neoatherosclerosis: overview of histopathologic findings and implications for intravascular imaging assessment. Eur. Hear. J. 36(32), 2147–2159 (2015)CrossRefGoogle Scholar
  9. 9.
    Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS, pp. 91–99 (2015)Google Scholar
  10. 10.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
  11. 11.
    Ughi, G.J., et al.: Automatic segmentation of in-vivo intra-coronary optical coherence tomography images to assess stent strut apposition and coverage. Int. J. Cardiovasc. Imagin 28(2), 229–241 (2012)CrossRefGoogle Scholar
  12. 12.
    Wang, A., et al.: Automatic stent strut detection in intravascular optical coherence tomographic pullback runs. Int. J. Cardiovasc. Imaging 29(1), 29–38 (2013)CrossRefGoogle Scholar
  13. 13.
    Wang, A., et al.: 3-D stent detection in intravascular OCT using a Bayesian network and graph search. IEEE Trans. Med. Imaging 34(7), 1549–1561 (2015)CrossRefGoogle Scholar
  14. 14.
    Yong, Y.L., et al.: Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography. J. Biomed. Opt. 22(12), 126005 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yuyu Guo
    • 1
  • Lei Bi
    • 2
  • Ashnil Kumar
    • 2
  • Yue Gao
    • 3
  • Ruiyan Zhang
    • 3
  • Dagan Feng
    • 2
  • Qian Wang
    • 1
    Email author
  • Jinman Kim
    • 2
  1. 1.Institute for Medical Imaging Technology, School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.School of Computer ScienceUniversity of SydneySydneyAustralia
  3. 3.Ruijin HospitalShanghai Jiaotong University School of MedicineShanghaiChina

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