Automated Segmentation of Bioresorbable Vascular Scaffold Struts in Intracoronary Optical Coherence Tomography Images
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nihms-984600.pdf
Description
Accepted version
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1.34 MB
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Author(s)
Athanasiou, Lambros S.
Rikhtegar Nezami, Farhad
de la Torre Hernández, Jose
Edelman, Elazer R
Date Issued
October 2017
Journal
2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Amrute, Junedh M. et al. “Automated Segmentation of Bioresorbable Vascular Scaffold Struts in Intracoronary Optical Coherence Tomography Images.” Paper presented at the 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE), Washington, D.C., 23-25 Oct. 2017, IEEE © 2017 The Author(s)
Version
Author's final manuscript
Abstract
Bioresorbable vascular scaffolds (BVS), the next step in the continuum of minimally invasive vascular interventions present new opportunities for patients and clinicians but challenges as well. As they are comprised of polymeric materials standard imaging is challenging. This is especially problematic as modalities like optical coherence tomography (OCT) become more prevalent in cardiology. OCT, a light-based intracoronary imaging technique, provides cross-sectional images of plaque and luminal morphology. Until recently segmentation of OCT images for BVS struts was performed manually by experts. However, this process is time consuming and not tractable for large amounts of patient data. Several automated methods exist to segment metallic stents, which do not apply to the newer BVS. Given this current limitation coupled with the emerging popularity of the BVS technology, it is crucial to develop an automated methodology to segment BVS struts in OCT images. The objective of this paper is to develop a novel BVS strut detection method in intracoronary OCT images. First, we pre-process the image to remove imaging artifacts. Then, we use a K-means clustering algorithm to automatically segment the image. Finally, we isolate the stent struts from the rest of the image. The accuracy of the proposed method was evaluated using expert estimations on 658 annotated images acquired from 7 patients at the time of coronary arterial interventions. Our proposed methodology has a positive predictive value of 0.93, a Pearson Correlation coefficient of 0.94, and a F1 score of 0.92. The proposed methodology allows for rapid, accurate, and fully automated segmentation of BVS struts in OCT images.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/BIBE.2017.00-38
