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

Yazan Gharaibeh; Pengfei Dong; David Prabhu; Chaitanya Kolluru; Juhwan Lee; Vlad Zimin; Hozhabr Mozafari; Hiram Bizzera; Linxia Gu; David Wilson

doi:10.1117/12.2515256

8 March 2019 Deep learning segmentation of coronary calcified plaque from intravascular optical coherence tomography (IVOCT) images with application to finite element modeling of stent deployment

Yazan Gharaibeh, Pengfei Dong, David Prabhu, Chaitanya Kolluru, Juhwan Lee, Vlad Zimin, Hozhabr Mozafari, Hiram Bizzera, Linxia Gu, David Wilson

Author Affiliations +

Proceedings Volume 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling; 109511C (2019) https://doi.org/10.1117/12.2515256
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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).

Conference Presentation

Citation Download Citation

Yazan Gharaibeh, Pengfei Dong, David Prabhu, Chaitanya Kolluru, Juhwan Lee, Vlad Zimin, Hozhabr Mozafari, Hiram Bizzera, Linxia Gu, and David Wilson "Deep learning segmentation of coronary calcified plaque from intravascular optical coherence tomography (IVOCT) images with application to finite element modeling of stent deployment", Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 109511C (8 March 2019); https://doi.org/10.1117/12.2515256

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available