Automated detection of vessel lumen and stent struts in intravascular optical coherence tomography to evaluate stent apposition and neointimal coverage. Issue 4 (14th March 2016)

• Record Type: Journal Article
• Authors: Nam, Hyeong Soo; Kim, Chang‐Soo; Lee, Jae Joong; Song, Joon Woo; Kim, Jin Won; Yoo, Hongki
• Is Part Of: Medical physics. Volume 43:Issue 4(2016)
• Page Start: 1662
• Page End: 1675
• Publication Date: 2016-03-14
• Subjects: blood vessels -- cardiovascular system -- image segmentation -- medical image processing -- neural nets -- object detection -- optical tomography -- stents; Visual imaging -- Segmentation -- Smart prosthetics -- Neural engineering -- Biomedical engineering; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; optical coherence tomography -- stent thrombosis -- image segmentation -- artificial neural network -- stent malapposition -- neointimal covearge; Optical coherence tomography -- Medical image artifacts -- Vascular system -- Tissues -- Medical image smoothing -- Coherence imaging -- Medical image segmentation -- Artificial neural networks; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4943374
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Intravascular optical coherence tomography (IV‐OCT) is a high‐resolution imaging method used to visualize the microstructure of arterial walls in vivo . IV‐OCT enables the clinician to clearly observe and accurately measure stent apposition and neointimal coverage of coronary stents, which are associated with side effects such as in‐stent thrombosis. In this study, the authors present an algorithm for quantifying stent apposition and neointimal coverage by automatically detecting lumen contours and stent struts in IV‐OCT images. Methods: The algorithm utilizes OCT intensity images and their first and second gradient images along the axial direction to detect lumen contours and stent strut candidates. These stent strut candidates are classified into true and false stent struts based on their features, using an artificial neural network with one hidden layer and ten nodes. After segmentation, either the protrusion distance (PD) or neointimal thickness (NT) for each strut is measured automatically. In randomly selected image sets covering a large variety of clinical scenarios, the results of the algorithm were compared to those of manual segmentation by IV‐OCT readers. Results: Stent strut detection showed a 96.5% positive predictive value and a 92.9% true positive rate. In addition, case‐by‐case validation also showed comparable accuracy for most cases. High correlation coefficients ( R > 0.99) were observed for PD and NT between the algorithmic and the manual results, showing little bias (0.20 and 0.46 μ m, respectively) and a narrow range of limits of agreement (36 and 54 μ m, respectively). In addition, the algorithm worked well in various clinical scenarios and even in cases with a low level of stent malapposition and neointimal coverage. Conclusions: The presented automatic algorithm enables robust and fast detection of lumen contours and stent struts and provides quantitative measurements of PD and NT. In addition, the algorithm was validated using various clinical cases to demonstrate its reliability. Therefore, this technique can be effectively utilized for clinical trials on stent‐related side effects, including in‐stent thrombosis and in‐stent restenosis.