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Automated Segmentation of Bioresorbable Vascular Scaffold Struts in Intracoronary Optical Coherence Tomography Images

Amrute, Junedh M. and Athanasiou, Lambros and Rikhtegar, Farhad and de la Torre Hernández, José M. and Garcia Camarero, Tamara and Edelman, Elazer R. (2017) Automated Segmentation of Bioresorbable Vascular Scaffold Struts in Intracoronary Optical Coherence Tomography Images. In: 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE , Piscataway, NJ, pp. 297-302. ISBN 978-1-5386-1324-5. http://resolver.caltech.edu/CaltechAUTHORS:20180412-103559842

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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.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/BIBE.2017.00-38DOIArticle
https://ieeexplore.ieee.org/document/8251306PublisherArticle
Additional Information:© 2017 IEEE. This project was supported in part by funds to JMA from the Caltech Franz and Anne Nierlich Summer Undergraduate Research Fellowship, Vergottis Fellowship at Harvard Medical School awarded to LA, and R01 support from the National Institutes of Health (GM 49039) to FR and ERE.
Funders:
Funding AgencyGrant Number
Caltech Summer Undergraduate Research Fellowship (SURF)UNSPECIFIED
Harvard Medical SchoolUNSPECIFIED
NIHGM 49039
Subject Keywords:Optical Coherence Tomography (OCT), Bioresorbable Vascular Scaffold (BVS), K-means clustering
Record Number:CaltechAUTHORS:20180412-103559842
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20180412-103559842
Official Citation:J. M. Amrute, L. Athanasiou, F. Rikhtegar, J. M. d. l. T. Hernandez, T. G. Camarero and E. R. Edelman, "Automated Segmentation of Bioresorbable Vascular Scaffold Struts in Intracoronary Optical Coherence Tomography Images," 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE), Washington, DC, 2017, pp. 297-302. doi: 10.1109/BIBE.2017.00-38. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8251306&isnumber=8251248
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:85778
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:12 Apr 2018 20:30
Last Modified:12 Apr 2018 20:30

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