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Automated segmentation of metal and BVS stent struts from OCT images using U-Net

Percutaneous Coronary Intervention (PCI) is an effective treatment for coronary artery diseases. PCI treatment is usually carried out with stent implantation to provide structural support to balloon dilated blood vessel, reducing risk of re-narrowing. Intravascular Optical Coherence Tomography (OCT)...

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Main Authors: Lau, Yu Shi, Tan, Li Kuo, Chan, Chow Khuen, Chee, Kok Han, Liew, Yih Miin
Format: Conference or Workshop Item
Published: Springer Science and Business Media Deutschland GmbH 2022
Subjects:
R Medicine
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.um.edu.my/43471/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129266521&doi=10.1007%2f978-3-030-90724-2_8&partnerID=40&md5=4befd6ae4dfa8c09eabe730e75fc3e18
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spelling my.um.eprints.434712025-02-10T08:15:25Z http://eprints.um.edu.my/43471/ Automated segmentation of metal and BVS stent struts from OCT images using U-Net Lau, Yu Shi Tan, Li Kuo Chan, Chow Khuen Chee, Kok Han Liew, Yih Miin R Medicine TA Engineering (General). Civil engineering (General) Percutaneous Coronary Intervention (PCI) is an effective treatment for coronary artery diseases. PCI treatment is usually carried out with stent implantation to provide structural support to balloon dilated blood vessel, reducing risk of re-narrowing. Intravascular Optical Coherence Tomography (OCT) can provide a series of cross-section images depicting the internal structure of the artery and residing stent during PCI treatment. Stent struts segmentation for OCT images is necessary to provide quantitative data regarding quality of stent deployment during PCI and severity of restenosis during follow-up examination. Manual segmentation of stent struts is not efficient and infeasible due to large number of stent struts presented in each pullback of OCT images. Thus, automated stent struts segmentation is necessary to help clinicians in getting quantified data from OCT images within intraoperative time frame. In this paper, an automated stent strut segmentation algorithm was developed, utilizing 3D information of stent structure and state-of-the-art U-Net. The implementation of the algorithm preserves the spatial resolution of the full-size OCT images without down-sampling. The algorithm was trained and tested on both Bioresorbable Vascular Scaffold (BVS) and metal stent images. It achieved Dice’s coefficient of 0.82 for BVS images, precision of 0.90 and recall of 0.85 for metal stent images. This algorithm works for both BVS and metal stents OCT images and adapts to different stent conditions. © 2022, Springer Nature Switzerland AG. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed Lau, Yu Shi and Tan, Li Kuo and Chan, Chow Khuen and Chee, Kok Han and Liew, Yih Miin (2022) Automated segmentation of metal and BVS stent struts from OCT images using U-Net. In: 6th Kuala Lumpur International Conference on Biomedical Engineering, BioMed 2021, 28-29 July 2021, Virtual, Online. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129266521&doi=10.1007%2f978-3-030-90724-2_8&partnerID=40&md5=4befd6ae4dfa8c09eabe730e75fc3e18
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic R Medicine
TA Engineering (General). Civil engineering (General)
spellingShingle R Medicine
TA Engineering (General). Civil engineering (General)
Lau, Yu Shi
Tan, Li Kuo
Chan, Chow Khuen
Chee, Kok Han
Liew, Yih Miin
Automated segmentation of metal and BVS stent struts from OCT images using U-Net
description Percutaneous Coronary Intervention (PCI) is an effective treatment for coronary artery diseases. PCI treatment is usually carried out with stent implantation to provide structural support to balloon dilated blood vessel, reducing risk of re-narrowing. Intravascular Optical Coherence Tomography (OCT) can provide a series of cross-section images depicting the internal structure of the artery and residing stent during PCI treatment. Stent struts segmentation for OCT images is necessary to provide quantitative data regarding quality of stent deployment during PCI and severity of restenosis during follow-up examination. Manual segmentation of stent struts is not efficient and infeasible due to large number of stent struts presented in each pullback of OCT images. Thus, automated stent struts segmentation is necessary to help clinicians in getting quantified data from OCT images within intraoperative time frame. In this paper, an automated stent strut segmentation algorithm was developed, utilizing 3D information of stent structure and state-of-the-art U-Net. The implementation of the algorithm preserves the spatial resolution of the full-size OCT images without down-sampling. The algorithm was trained and tested on both Bioresorbable Vascular Scaffold (BVS) and metal stent images. It achieved Dice’s coefficient of 0.82 for BVS images, precision of 0.90 and recall of 0.85 for metal stent images. This algorithm works for both BVS and metal stents OCT images and adapts to different stent conditions. © 2022, Springer Nature Switzerland AG.
format Conference or Workshop Item
author Lau, Yu Shi
Tan, Li Kuo
Chan, Chow Khuen
Chee, Kok Han
Liew, Yih Miin
author_facet Lau, Yu Shi
Tan, Li Kuo
Chan, Chow Khuen
Chee, Kok Han
Liew, Yih Miin
author_sort Lau, Yu Shi
title Automated segmentation of metal and BVS stent struts from OCT images using U-Net
title_short Automated segmentation of metal and BVS stent struts from OCT images using U-Net
title_full Automated segmentation of metal and BVS stent struts from OCT images using U-Net
title_fullStr Automated segmentation of metal and BVS stent struts from OCT images using U-Net
title_full_unstemmed Automated segmentation of metal and BVS stent struts from OCT images using U-Net
title_sort automated segmentation of metal and bvs stent struts from oct images using u-net
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2022
url http://eprints.um.edu.my/43471/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129266521&doi=10.1007%2f978-3-030-90724-2_8&partnerID=40&md5=4befd6ae4dfa8c09eabe730e75fc3e18
_version_ 1825160594957795328
score 13.244413

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