Deep learning segmentation of coronary calcified plaque from intravascular optical coherence tomography (IVOCT) images with application to finite element modeling of stent deployment

2019 ◽  
Author(s):  
Yazan Gharaibeh ◽  
Pengfei Dong ◽  
David Prabhu ◽  
Chaitanya Kolluru ◽  
Juhwan Lee ◽  
...  
Keyword(s):  
Finite Element ◽  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Finite Element Modeling ◽  
Optical Coherence ◽  
Stent Deployment ◽  
Element Modeling ◽  
Calcified Plaque ◽  
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.

Automated segmentation of metal stent and bioresorbable vascular scaffold in intravascular optical coherence tomography images using deep learning architectures

2021 ◽  
Author(s):  
Yu Shi Lau ◽  
Li Kuo Tan ◽  
Chow Khuen Chan ◽  
Kok Han Chee ◽  
Yih Miin Liew
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Window Size ◽  
Metal Stents ◽  
Automated Segmentation ◽  
Optical Coherence ◽  
Metal Stent ◽  
Bioresorbable Vascular Scaffold ◽  
Stent Deployment ◽  
Intravascular Optical Coherence Tomography

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 observers 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.

Machine (Deep) Learning and Finite Element Modeling

2021 ◽  
pp. 183-188
Author(s):  
Yan-Ting Lee ◽  
Tai-Hsien Wu ◽  
Mei-Ling Lin ◽  
Ching-Chang Ko
Keyword(s):  
Finite Element ◽  
Deep Learning ◽  
Finite Element Modeling ◽  
Element Modeling

Exact First Order Finite Element Modeling for Eddy Current NDE

1991 ◽  
Vol 3 (1) ◽  
pp. 235-253 ◽  
Author(s):  
L. D. Philipp ◽  
Q. H. Nguyen ◽  
D. D. Derkacht ◽  
D. J. Lynch ◽  
A. Mahmood
Keyword(s):  
Finite Element ◽  
Finite Element Modeling ◽  
Eddy Current ◽  
Element Modeling ◽  
First Order ◽  
Eddy Current Nde
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