Share Email Print

Proceedings Paper

Deep learning segmentation of coronary calcified plaque from intravascular optical coherence tomography (IVOCT) images with application to finite element modeling of stent deployment
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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 vesselspecific 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).

Paper Details

Date Published: 8 March 2019
PDF: 10 pages
Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 109511C (8 March 2019); doi: 10.1117/12.2515256
Show Author Affiliations
Yazan Gharaibeh, Case Western Reserve Univ. (United States)
Pengfei Dong, Univ. of Nebraska-Lincoln (United States)
David Prabhu, Case Western Reserve Univ. (United States)
Chaitanya Kolluru, Case Western Reserve Univ. (United States)
Juhwan Lee, Case Western Reserve Univ. (United States)
Vlad Zimin, Univ. Hospitals (United States)
Hozhabr Mozafari, Univ. of Nebraska-Lincoln (United States)
Hiram Bizzera, Univ. Hospitals (United States)
Linxia Gu, Univ. of Nebraska-Lincoln (United States)
David Wilson, Case Western Reserve Univ. (United States)

Published in SPIE Proceedings Vol. 10951:
Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?