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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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).

Original languageEnglish (US)
Title of host publicationMedical Imaging 2019
Subtitle of host publicationImage-Guided Procedures, Robotic Interventions, and Modeling
EditorsBaowei Fei, Cristian A. Linte
PublisherSPIE
ISBN (Electronic)9781510625495
DOIs
StatePublished - 2019
EventMedical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling - San Diego, United States
Duration: Feb 17 2019Feb 19 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10951
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling
CountryUnited States
CitySan Diego
Period2/17/192/19/19

Keywords

  • Calcified plaque
  • Deep learning
  • Finite element model (FEM)
  • Intravascular optical coherence tomography (IVOCT)
  • Semantic segmentation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Gharaibeh, Y., Dong, P., Prabhu, D., Kolluru, C., Lee, J., Zimin, V., Mozafari, H., Bizzera, H., Gu, L., & Wilson, D. (2019). Deep learning segmentation of coronary calcified plaque from intravascular optical coherence tomography (IVOCT) images with application to finite element modeling of stent deployment. In B. Fei, & C. A. Linte (Eds.), Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling [109511C] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10951). SPIE. https://doi.org/10.1117/12.2515256