Automated peroperative assessment of stents apposition from OCT pullbacks

Dubuisson, Florian; Péry, Émilie; Ouchchane, Lemlih; Combaret, Nicolas; Kauffmann, Claude; Motreff, Pascal; Sarry, Laurent

doi:10.1016/j.compbiomed.2014.12.012

Cited by 9 publications

(12 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Table 1 reports a list as comprehensive as possible of the studies published on this topic. While several studies presented a detection algorithm of the lumen contours [ 13 , 15 , 18 , 19 , 22 , 26 , 30 , 31 , 35 , 36 , 39 , 40 ] or the stent struts [ 20 , 21 , 23 , 24 , 27 , 29 , 32 ] only, in other works both detection algorithms were developed [ 14 , 16 , 17 , 25 , 28 , 33 , 34 , 37 , 38 , 41 , 42 ].…”

Section: Automatic Segmentation Methods Of Oct Imagesmentioning

confidence: 99%

Patient-Specific Modeling of Stented Coronary Arteries Reconstructed from Optical Coherence Tomography: Towards a Widespread Clinical Use of Fluid Dynamics Analyses

Chiastra

Migliori

Burzotta

et al. 2017

J. of Cardiovasc. Trans. Res.

View full text Add to dashboard Cite

The recent widespread application of optical coherence tomography (OCT) in interventional cardiology has improved patient-specific modeling of stented coronary arteries for the investigation of local hemodynamics. In this review, the workflow for the creation of fluid dynamics models of stented coronary arteries from OCT images is presented. The algorithms for lumen contours and stent strut detection from OCT as well as the reconstruction methods of stented geometries are discussed. Furthermore, the state of the art of studies that investigate the hemodynamics of OCT-based stented coronary artery geometries is reported. Although those studies analyzed few patient-specific cases, the application of the current reconstruction methods of stented geometries to large populations is possible. However, the improvement of these methods and the reduction of the time needed for the entire modeling process are crucial for a widespread clinical use of the OCT-based models and future in silico clinical trials.

show abstract

“…This automated method achieved 0.91 recall and 0.84 precision. Another automatic method for the detection and assessment of struts apposition in OCT images was presented classifying the struts in 3 categories (covered, uncovered, malapposed) [4]. A four step algorithm was recently developed for preprocessing, lumen border detection, stent strut detection and 3D point cloud creation [5].…”

Section: Introductionmentioning

confidence: 99%

A Novel Methodology for Detection of Lumen, Outer Wall, Plaques and Stent Struts in Coronary Arteries Using Optical Coherence Tomography

Kyriakidis

Sakellarios

Karanasiou

et al. 2019

2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)

View full text Add to dashboard Cite

In this work, we present a novel and accurate methodology for the segmentation of optical coherence tomography imaging (OCT) and detection of lumen and outer wall, plaque characterization and stent struts in stented arteries. In particular, the methodology starts with pre-processing and detection of the catheter artefact. Struts detection is based on the identification of the size of the shadow behind the struts. Our methodology can be applied to metal stents as well as to polymeric and bioresorbable vascular scaffold (BVS) stents. Lumen segmentation is based on Fuzzy clustering and Fast marching on the gradient image to find the shortest path. The outer wall is segmented using a methodology, which combines K-means and 3-dimensional (3D) surface fitting on the detected edges. K-means with 3 clusters is performed at the final step on the ROI between the lumen and outer border of adventitia to characterize the plaque type. The validation is achieved by comparing the algorithm's results with manual annotations provided by experts. The results demonstrate that our methodology is accurate in lumen (R=0.99) and outer wall segmentation (R=0.77) and struts detection (R=0.82). The average Hausdorff distance and the Dice Similarity for lumen segmentation is 0.097 mm and 0.96, respectively.

show abstract

Automated peroperative assessment of stents apposition from OCT pullbacks

Cited by 9 publications

References 25 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

Patient-Specific Modeling of Stented Coronary Arteries Reconstructed from Optical Coherence Tomography: Towards a Widespread Clinical Use of Fluid Dynamics Analyses

A Novel Methodology for Detection of Lumen, Outer Wall, Plaques and Stent Struts in Coronary Arteries Using Optical Coherence Tomography

Contact Info

Product

Resources

About