Automated segmentation of metal and BVS stent struts from OCT images using U-Net
Document Type
Conference Item
Publication Date
1-1-2022
Abstract
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.
Keywords
Automation, Deep learning, Diseases, Image segmentation, Medical computing, Medical imaging, Metals, Optical tomography, Struts, Automated segmentation, Bioresorbable, Coronary artery disease, Deep learning, Metal stents, Percutaneous coronary intervention, Stent implantation, Stent strut segmentation, U-net, Vascular scaffolds, Stents
Divisions
biomedengine,medicinedept
Funders
Malaysia Ministry of Higher Education Fundamental Research Grant Scheme [Grant no. FRGS/1/2018/SKK03/UM/02/1, GPF026A-2019]
Publication Title
IFMBE Proceedings
Volume
86
Publisher
Springer Science and Business Media Deutschland GmbH
Event Title
6th Kuala Lumpur International Conference on Biomedical Engineering, BioMed 2021
Event Location
Virtual, Online
Event Dates
28-29 July 2021
Event Type
conference