Polymeric endovascular strut and lumen detection algorithm for intracoronary optical coherence tomography images
Polymeric endovascular implants are the next step in minimally invasive vascular interventions. As an alternative to traditional metallic drug-eluting stents, these often-erodible scaffolds present opportunities and challenges for patients and clinicians. Theoretically, as they resorb and are absorbed over time, they obviate the long-term complications of permanent implants, but in the short-term visualization and therefore positioning is problematic. Polymeric scaffolds can only be fully imaged using optical coherence tomography (OCT) imaging—they are relatively invisible via angiography—and segmentation of polymeric struts in OCT images is performed manually, a laborious and intractable procedure for large datasets. Traditional lumen detection methods using implant struts as boundary limits fail in images with polymeric implants. Therefore, it is necessary to develop an automated method to detect polymeric struts and luminal borders in OCT images; we present such a fully automated algorithm. Accuracy was validated using expert annotations on 1140 OCT images with a positive predictive value of 0.93 for strut detection and an R^2 correlation coefficient of 0.94 between detected and expert-annotated lumen areas. The proposed algorithm allows for rapid, accurate, and automated detection of polymeric struts and the luminal border in OCT images.
© 2018 Society of Photo-Optical Instrumentation Engineers. Paper 170542RRR received Aug. 15, 2017; accepted for publication Feb. 23, 2018; published online Mar. 20, 2018. This project was supported in part by funds from the Caltech Franz and Anne Nierlich Summer Undergraduate Research Fellowship awarded to J.M.A., George & Marie Vergottis Foundation Fellowship at Harvard Medical School awarded to L.A., and R01 support from the National Institutes of Health (GM 49039) awarded to F.R. and E.R.E. There are no conflicts of interest.
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