Journal
JOURNAL OF BIOMEDICAL OPTICS
Volume 22, Issue 12, Pages -Publisher
SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
DOI: 10.1117/1.JBO.22.12.126005
Keywords
optical coherence tomography; pattern recognition; neural network; segmentation; optical diagnostics; coronary lumen
Funding
- University of Malaya Research Grant [RP028A-14HTM]
- University of Malaya Postgraduate Research Grant [PG052-2015B]
- Premier's Research and Industry Fund - South Australian Government Department of State Development
- Australian Research Council [CE140100003, DP150104660]
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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. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE).
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