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

Authors

Yazan Gharaibeh, Case Western Reserve University
Pengfei Dong, University of Nebraska–Lincoln
David Prabhu, Case Western Reserve University
Chaitanya Kolluru, Case Western Reserve University
Juhwan Lee, Case Western Reserve University
Vlad Zimin, Harrington Heart and Vascular Institute
Hozhabr Mozafari, University of Nebraska–Lincoln
Hiram Bizzera, Harrington Heart and Vascular Institute
Linxia Gu, University of Nebraska–Lincoln
David Wilson, Case Western Reserve University

Document Type

Article

Date of this Version

2-2019

Citation

Proc SPIE Int Soc Opt Eng. 2019 February ; 10951: . doi:10.1117/12.2515256.

Comments

Public Access

Abstract

Because coronary artery calcified plaques can hinder or eliminate stent deployment, interventional cardiologists need a better way to plan interventions, which might include one of the many methods for calcification modification (e.g., atherectomy). We are imaging calcifications with intravascular optical coherence tomography (IVOCT), which is the lone intravascular imaging technique with the ability to image the extent of a calcification, and using results to build vessel-specific finite element models for stent deployment. We applied methods to a large set of image data (>45 lesions and > 2,600 image frames) of calcified plaques, manually segmented by experts into calcified, lumen and “other” tissue classes. In optimization experiments, we evaluated anatomical (x, y) versus acquisition (r,θ) views, augmentation methods, and classification noise cleaning. Noisy semantic segmentations are cleaned by applying a conditional random field (CRF). We achieve an accuracy of 0.85 ± 0.04, 0.99 ± 0.01, and 0.97 ± 0.01, and F-score of 0.88 ± 0.07, 0.97 ± 0.01, and 0.91 ± 0.04 for calcified, lumen, and other tissues classes respectively across all folds following CRF noise cleaning. As a proof of concept, we applied our methods to cadaver heart experiments on highly calcified plaques. Following limited manual correction, we used our calcification segmentations to create a lesion-specific finite element model (FEM) and used it to predict direct stenting deployment at multiple pressure steps. FEM modeling of stent deployment captured many features found in the actual stent deployment (e.g., lumen shape, lumen area, and location and number of apposed stent struts).