Journal
MEDICAL IMAGING 2019: IMAGE-GUIDED PROCEDURES, ROBOTIC INTERVENTIONS, AND MODELING
Volume 10951, Issue -, Pages -Publisher
SPIE-INT SOC OPTICAL ENGINEERING
DOI: 10.1117/12.2515256
Keywords
intravascular optical coherence tomography (IVOCT); deep learning; semantic segmentation; calcified plaque; finite element model (FEM)
Funding
- National Heart, Lung, and Blood Institute [NIH R01HL114406-01]
- Case Western Reserve University
- NIH construction grant [C06RR12463]
- University Hospitals of Cleveland
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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,theta) 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).
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