• 1 University of Malaya, Faculty of Engineering, Department of Biomedical Engineering, Kuala Lumpur, Malaysia
  • 2 University of Malaya, Faculty of Medicine, Department of Biomedical Imaging, Kuala Lumpur, Malaysia
  • 3 University of Adelaide, Faculty of Health and Medical Sciences, Adelaide Medical School, Australian, Australia
  • 4 University of Malaya, Faculty of Medicine, Department of Medicine, Kuala Lumpur, Malaysia
J Biomed Opt, 2017 12;22(12):1-9.
PMID: 29274144 DOI: 10.1117/1.JBO.22.12.126005


Intravascular optical coherence tomography (OCT) is an optical imaging modality commonly used in the assessment of coronary artery diseases during percutaneous coronary intervention. Manual segmentation to assess luminal stenosis from OCT pullback scans is challenging and time consuming. We propose a linear-regression convolutional neural network to automatically perform vessel lumen segmentation, parameterized in terms of radial distances from the catheter centroid in polar space. Benchmarked against gold-standard manual segmentation, our proposed algorithm achieves average locational accuracy of the vessel wall of 22 microns, and 0.985 and 0.970 in Dice coefficient and Jaccard similarity index, respectively. The average absolute error of luminal area estimation is 1.38%. The processing rate is 40.6 ms per image, suggesting the potential to be incorporated into a clinical workflow and to provide quantitative assessment of vessel lumen in an intraoperative time frame.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.