TY - GEN
T1 - Deep learning segmentation of coronary calcified plaque from intravascular optical coherence tomography (IVOCT) images with application to finite element modeling of stent deployment
AU - Gharaibeh, Yazan
AU - Dong, Pengfei
AU - Prabhu, David
AU - Kolluru, Chaitanya
AU - Lee, Juhwan
AU - Zimin, Vlad
AU - Mozafari, Hozhabr
AU - Bizzera, Hiram
AU - Gu, Linxia
AU - Wilson, David
N1 - Publisher Copyright:
© 2019 SPIE.
PY - 2019
Y1 - 2019
N2 - 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).
AB - 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).
KW - Calcified plaque
KW - Deep learning
KW - Finite element model (FEM)
KW - Intravascular optical coherence tomography (IVOCT)
KW - Semantic segmentation
UR - http://www.scopus.com/inward/record.url?scp=85068907590&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068907590&partnerID=8YFLogxK
U2 - 10.1117/12.2515256
DO - 10.1117/12.2515256
M3 - Conference contribution
C2 - 35978855
AN - SCOPUS:85068907590
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2019
A2 - Fei, Baowei
A2 - Linte, Cristian A.
PB - SPIE
T2 - Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling
Y2 - 17 February 2019 through 19 February 2019
ER -