24 March 2016 Inner and outer coronary vessel wall segmentation from CCTA using an active contour model with machine learning-based 3D voxel context-aware image force
Author Affiliations +
Abstract
In this paper, we present a fully automated approach to coronary vessel segmentation, which involves calcification or soft plaque delineation in addition to accurate lumen delineation, from 3D Cardiac Computed Tomography Angiography data. Adequately virtualizing the coronary lumen plays a crucial role for simulating blood ow by means of fluid dynamics while additionally identifying the outer vessel wall in the case of arteriosclerosis is a prerequisite for further plaque compartment analysis. Our method is a hybrid approach complementing Active Contour Model-based segmentation with an external image force that relies on a Random Forest Regression model generated off-line. The regression model provides a strong estimate of the distance to the true vessel surface for every surface candidate point taking into account 3D wavelet-encoded contextual image features, which are aligned with the current surface hypothesis. The associated external image force is integrated in the objective function of the active contour model, such that the overall segmentation approach benefits from the advantages associated with snakes and from the ones associated with machine learning-based regression alike. This yields an integrated approach achieving competitive results on a publicly available benchmark data collection (Rotterdam segmentation challenge).
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Udhayaraj Sivalingam, Udhayaraj Sivalingam, Michael Wels, Michael Wels, Markus Rempfler, Markus Rempfler, Stefan Grosskopf, Stefan Grosskopf, Michael Suehling, Michael Suehling, Bjoern H. Menze, Bjoern H. Menze, } "Inner and outer coronary vessel wall segmentation from CCTA using an active contour model with machine learning-based 3D voxel context-aware image force", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 978502 (24 March 2016); doi: 10.1117/12.2216200; https://doi.org/10.1117/12.2216200
PROCEEDINGS
8 PAGES


SHARE
Back to Top