scholarly journals Voxel-based plaque classification in coronary intravascular optical coherence tomography images using decision trees

2018 ◽  
Author(s):  
Chaitanya Kolluru ◽  
David Prabhu ◽  
Yazan Gharaibeh ◽  
Hao Wu ◽  
David L. Wilson
Keyword(s):  
Optical Coherence Tomography ◽  
Decision Trees ◽  
Optical Coherence ◽  
Plaque Classification ◽  
Intravascular Optical Coherence Tomography

scholarly journals Automated Volumetric Intravascular Plaque Classification Using Optical Coherence Tomography

AI Magazine ◽  
2017 ◽  
Vol 38 (1) ◽  
pp. 61-72 ◽  
Author(s):  
Ronny Shalev ◽  
Daisuke Nakamura ◽  
Setsu Nishino ◽  
Andrew Rollins ◽  
Hiram Bezerra ◽  
...  
Keyword(s):  
At Risk ◽  
Optical Coherence Tomography ◽  
Human Error ◽  
3D Visualization ◽  
Optical Coherence ◽  
Fibrous Cap ◽  
Plaque Classification ◽  
Coronary Events ◽  
Intravascular Optical Coherence Tomography ◽  
Interactive 3D

An estimated 17.5 million people died from a cardiovascular disease in 2012, representing 31 percent of all global deaths. Most acute coronary events result from rupture of the protective fibrous cap overlying an atherosclerotic plaque. The task of early identification of plaque types that can potentially rupture is, therefore, of great importance. The state-of-the-art approach to imaging blood vessels is intravascular optical coherence tomography (IVOCT). However, currently, this is an offline approach where the images are first collected and then manually analyzed an image at a time to identify regions at risk of thrombosis. This process is extremely laborious, time consuming and prone to human error. We are building a system that, when complete, will provide interactive 3D visualization of a blood vessel as an IVOCT is in progress. The visualization will highlight different plaque types and enable quick identification of regions at risk for thrombosis. In this paper, we describe our approach, focusing on machine learning methods that are a key enabling technology. Our empirical results using real OCT data show that our approach can identify different plaque types efficiently with high accuracy across multiple patients.

Optical coherence tomography for elucidation of flow-diversion phenomena: The concept of endothelized mural thrombus behind reversible in-stent stenosis in flow-diverters

2021 ◽  
pp. 159101992110034
Author(s):  
Andre Monteiro ◽  
Demetrius K Lopes ◽  
Amin Aghaebrahim ◽  
Ricardo Hanel
Keyword(s):  
Optical Coherence Tomography ◽  
Intracranial Aneurysms ◽  
Flow Diversion ◽  
Optical Coherence ◽  
Recurrence Rates ◽  
Oct Imaging ◽  
Intravascular Optical Coherence Tomography ◽  
Stenotic Area ◽  
Flow Diverters ◽  
Stent Stenosis

Purpose Flow-diverters have revolutionized the endovascular treatment of intracranial aneurysms, offering a durable solution to aneurysms with high recurrence rates after conventional stent-assisted coiling. Events that occur after treatment with flow-diversion, such as in-stent stenosis (ISS) are not well understood and require further assessment. After assessing an animal model with Optical Coherence Tomography (OCT), we propose a concept that could explain the mechanism causing reversible ISS after treatment of intracranial aneurysms with flow-diverters. Methods Six Pipeline Flex embolization devices (PED-Flex), six PED with Shield technology (PED-Shield), and four Solitaire AB devices were implanted in the carotid arteries (two stents per vessel) of four pigs. Intravascular optical coherence tomography (OCT) and digital subtraction angiography (DSA) images obtained on day 21 were compared to histological specimens. Results A case of ISS in a PED-Flex device was assessed with OCT imaging. Neointima with asymmetrical topography completely covering the PED struts was observed. Histological preparations of the stenotic area demonstrated thrombus on the surface of device struts, covered by neointima. Conclusion This study provides a plausible concept for reversible ISS in flow-diverters. Based on an observation of a previous experiment, we propose that similar cases of ISS are related to thrombus presence underneath endothelization, but further experiments focused on this phenomenon are needed. Optical Coherence Tomography will be useful tool when available for clinical use.

Coronary Stent Reconstruction in Intravascular Optical Coherence Tomography

2021 ◽  
Vol 41 (4) ◽  
pp. 0417001
Author(s):  
刘铁根 Liu Tiegen ◽  
陶魁园 Tao Kuiyuan ◽  
丁振扬 Ding Zhenyang ◽  
刘琨 Liu Kun ◽  
江俊峰 Jiang Junfeng ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Coronary Stent ◽  
Optical Coherence ◽  
Intravascular Optical Coherence Tomography

Abstract 15219: Fully Automated Calcification Segmentation in Intravascular Optical Coherence Tomography Images Using a Two-step Deep Learning Approach

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Juhwan Lee ◽  
Yazan Gharaibeh ◽  
Vladislav N Zimin ◽  
Luis A Dallan ◽  
Hiram G Bezerra ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Learning Approach ◽  
Optical Coherence ◽  
Stent Deployment ◽  
Automated Method ◽  
Increased Risk ◽  
Maximum Angle ◽  
Intravascular Optical Coherence Tomography ◽  
Sensitivity Specificity

Introduction: Major calcifications are of great concern when performing percutaneous coronary intervention as they hinder stent deployment. Calcifications can lead to under-expansion and strut malapposition, with increased risk of thrombosis and in-stent restenosis. Therefore, accurate identification, visualization, and quantification of calcifications are important. Objective: In this study, we developed a 2-step deep learning approach to enable segmentation of major calcifications in a typical 500+ frame intravascular optical coherence tomography (IVOCT) images. Methods: The dataset consisted of a total of 12,551 IVOCT frames across 68 patients with 68 pullbacks. We applied a series of pre-processing steps including guidewire/shadow removal, lumen detection, pixel shifting, and Gaussian filtering. To detect the major calcifications in step 1, we implemented the 3D convolutional neural network consisting of 5 convolutional, 5 max-pooling, and 2 fully-connected layers. In step-2, SegNet deep learning model was used to segment calcified plaques. In both steps, classification errors were reduced using conditional random field. Results: Step-1 reliably identified major calcifications (sensitivity/specificity: 97.7%/87.7%). Semantic segmentation of calcifications following step-2 was typically visually quite good (Fig. 1) with (sensitivity/specificity: 86.2%/96.7%). Our method was superior to a single step approach and showed excellent reproducibility on repetitive IVOCT pullbacks, with very small differences of clinically relevant attributes (maximum angle, maximum thickness, and length) and the exact same IVOCT calcium scores for assessment of stent deployment. Conclusions: We developed the fully-automated method for identifying calcifications in IVOCT images based on a 2-step deep learning approach. Extensive analyses indicate that our method is very informative for both live-time treatment planning and research purposes.

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