Automated detection of vessel lumen and stent struts in intravascular optical coherence tomography to evaluate stent apposition and neointimal coverage

Nam, Hyunwoo; Kim, Chang Soo; Lee, Jae Joong; Song, Joon Woo; Kim, Jin Won; Yoo, Hongki

doi:10.1118/1.4943374

Cited by 46 publications

(42 citation statements)

References 53 publications

(60 reference statements)

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“…Over the last years, several methods have been proposed to analyze OCT datasets in order to automatically detect the lumen contours [8–22] and stent struts [9,10,16–18,20,22–27]. In general, those automatic algorithms showed good results with respect to manual analysis.…”

Section: Introductionmentioning

confidence: 99%

“…In general, those automatic algorithms showed good results with respect to manual analysis. However, algorithms for the identification of both lumen contours and stent struts, able to take into account the issues related to the presence of bifurcation branches, were reported only in few studies [10,16]. Moreover, validation of these algorithms was performed only against manual segmentation and their repeatability was not evaluated.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reconstruction of stented coronary arteries from optical coherence tomography images: Feasibility, validation, and repeatability of a segmentation method

et al. 2017

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Optical coherence tomography (OCT) is an established catheter-based imaging modality for the assessment of coronary artery disease and the guidance of stent placement during percutaneous coronary intervention. Manual analysis of large OCT datasets for vessel contours or stent struts detection is time-consuming and unsuitable for real-time applications. In this study, a fully automatic method was developed for detection of both vessel contours and stent struts. The method was applied to in vitro OCT scans of eight stented silicone bifurcation phantoms for validation purposes. The proposed algorithm comprised four main steps, namely pre-processing, lumen border detection, stent strut detection, and three-dimensional point cloud creation. The algorithm was validated against manual segmentation performed by two independent image readers. Linear regression showed good agreement between automatic and manual segmentations in terms of lumen area (r>0.99). No statistically significant differences in the number of detected struts were found between the segmentations. Mean values of similarity indexes were >95% and >85% for the lumen and stent detection, respectively. Stent point clouds of two selected cases, obtained after OCT image processing, were compared to the centerline points of the corresponding stent reconstructions from micro computed tomography, used as ground-truth. Quantitative comparison between the corresponding stent points resulted in median values of ~150 μm and ~40 μm for the total and radial distances of both cases, respectively. The repeatability of the detection method was investigated by calculating the lumen volume and the mean number of detected struts per frame for seven repeated OCT scans of one selected case. Results showed low deviation of values from the median for both analyzed quantities. In conclusion, this study presents a robust automatic method for detection of lumen contours and stent struts from OCT as supported by focused validation against both manual segmentation and micro computed tomography and by good repeatability.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reconstruction of stented coronary arteries from optical coherence tomography images: Feasibility, validation, and repeatability of a segmentation method

et al. 2017

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“…The automatic detection of the stent strut has also demonstrated high accuracy, allowing the application of dedicated programs to quantify stent malapposition (distance strut-wall) and neointimal coverage. These novel techniques can simplify stent strut analysis either in clinical or research applications 41 42…”

Section: Essential Ivus-oct Measurementsmentioning

confidence: 99%

Intracoronary Imaging

Giavarini

Kılıç

Diéguez

et al. 2017

Heart

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“…Commonly used handcrafted features and supervised approaches include shadow feature [11,12], decision trees [5] and wavelet based detection [3]. Besides, some studies used lumen segmentation [7,14] and stent shape models [1] to constrain the search space for the potential struts candidates. However, all these methods rely on effective pre-processing steps, such as denoising, illumination corrections and detecting lumen boundaries for producing accurate results, which thereby restricting its generalizability.…”

Section: Introductionmentioning

confidence: 99%

Deep Local-Global Refinement Network for Stent Analysis in IVOCT Images

Guo

Kumar

et al. 2019

Lecture Notes in Computer Science

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Implantation of stents into coronary arteries is a common treatment option for patients with cardiovascular disease. Assessment of safety and efficacy of the stent implantation occurs via manual visual inspection of the neointimal coverage from intravascular optical coherence tomography (IVOCT) images. However, such manual assessment requires the detection of thousands of strut points within the stent. This is a challenging, tedious, and time-consuming task because the strut points usually appear as small, irregular shaped objects with inhomogeneous textures, and are often occluded by shadows, artifacts, and vessel walls. Conventional methods based on textures, edge detection, or simple classifiers for automated detection of strut points in IVOCT images have low recall and precision as they are, unable to adequately represent the visual features of the strut point for detection. In this study, we propose a local-global refinement network to integrate local-patch content with global content for strut points detection from IVOCT images. Our method densely detects the potential strut points in local image patches and then refines them according to global appearance constraints to reduce false positives. Our experimental results on a clinical dataset of 7,000 IVOCT images demonstrated that our method outperformed the state-of-the-art methods with a recall of 0.92 and precision of 0.91 for strut points detection.

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Automated detection of vessel lumen and stent struts in intravascular optical coherence tomography to evaluate stent apposition and neointimal coverage

Cited by 46 publications

References 53 publications

Reconstruction of stented coronary arteries from optical coherence tomography images: Feasibility, validation, and repeatability of a segmentation method

Reconstruction of stented coronary arteries from optical coherence tomography images: Feasibility, validation, and repeatability of a segmentation method

Intracoronary Imaging

Deep Local-Global Refinement Network for Stent Analysis in IVOCT Images

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