Lau, Yu Shi and Tan, Li Kuo and Chan, Chow Khuen and Chee, Kok Han and Liew, Yih Miin (2021) Automated segmentation of metal stent and bioresorbable vascular scaffold in intravascular optical coherence tomography images using deep learning architectures. Physics in Medicine & Biology, 66 (24). p. 245026. ISSN 0031-9155, DOI https://doi.org/10.1088/1361-6560/ac4348.
Full text not available from this repository.Abstract
Percutaneous coronary intervention (PCI) with stent placement is a treatment effective for coronary artery diseases. Intravascular optical coherence tomography (OCT) with high resolution is used clinically to visualize stent deployment and restenosis, facilitating PCI operation and for complication inspection. Automated stent struts segmentation in OCT images is necessary as each pullback of OCT images could contain thousands of stent struts. In this paper, a deep learning framework is proposed and demonstrated for the automated segmentation of two major clinical stent types: metal stents and bioresorbable vascular scaffolds (BVS). U-Net, the current most prominent deep learning network in biomedical segmentation, was implemented for segmentation with cropped input. The architectures of MobileNetV2 and DenseNet121 were also adapted into U-Net for improvement in speed and accuracy. The results suggested that the proposed automated algorithm's segmentation performance approaches the level of independent human obsevers and is feasible for both types of stents despite their distinct appearance. U-Net with DenseNet121 encoder (U-Dense) performed best with Dice's coefficient of 0.86 for BVS segmentation, and precision/recall of 0.92/0.92 for metal stent segmentation under optimal crop window size of 256.
| Item Type: | Article |
|---|---|
| Funders: | Malaysia Ministry of Higher Education Fundamental Research Grant Scheme, Faculty Research Grant GPF |
| Uncontrolled Keywords: | Bioresorbable vascular scaffold; Deep learning; Metal stent; Optical coherence tomography; Stent strut segmentation |
| Subjects: | R Medicine > R Medicine (General) > Medical technology T Technology > TA Engineering (General). Civil engineering (General) |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 18 Feb 2022 08:20 |
| Last Modified: | 18 Feb 2022 08:20 |
| URI: | http://eprints.um.edu.my/id/eprint/26321 |
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