Automated algorithm for atlas-based segmentation of the heart and ...
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Automated algorithm for atlas-based segmentation of the heart and ...
Oct 12, 2010 - Damini Deya,b,*, Amit Ramesha, Piotr J Slomkaa,b, Ryo Nakazatoa, Victor Y Chenga, Guido. Germanoa,b, and Daniel S Berman a,b.
NIH Public Access Author Manuscript Proc SPIE. Author manuscript; available in PMC 2010 October 12.
NIH-PA Author Manuscript
Published in final edited form as: Proc SPIE. 2010 March 1; 7623: 762337–. doi:10.1117/12.844810.
Automated algorithm for atlas-based segmentation of the heart and pericardium from non-contrast CT Damini Deya,b,*, Amit Ramesha, Piotr J Slomkaa,b, Ryo Nakazatoa, Victor Y Chenga, Guido Germanoa,b, and Daniel S Bermana,b aDepartments of Imaging and Medicine, Cedars Sinai Medical Center, Los Angeles, CA bDepartment
of Medicine, David-Geffen School of Medicine at UCLA, Los Angeles, CA
Abstract
NIH-PA Author Manuscript
Automated segmentation of the 3D heart region from non-contrast CT is a pre-requisite for automated quantification of coronary calcium and pericardial fat. We aimed to develop and validate an automated, efficient atlas-based algorithm for segmentation of the heart and pericardium from noncontrast CT. A co-registered non-contrast CT atlas is first created from multiple manually segmented non-contrast CT data. Non-contrast CT data included in the atlas are co-registered to each other using iterative affine registration, followed by a deformable transformation using the iterative demons algorithm; the final transformation is also applied to the segmented masks. New CT datasets are segmented by first co-registering to an atlas image, and by voxel classification using a weighted decision function applied to all co-registered/pre-segmented atlas images. This automated segmentation method was applied to 12 CT datasets, with a co-registered atlas created from 8 datasets. Algorithm performance was compared to expert manual quantification. Cardiac region volume quantified by the algorithm (609.0 ± 39.8 cc) and the expert (624.4 ± 38.4 cc) were not significantly different (p=0.1, mean percent difference 3.8 ± 3.0%) and showed excellent correlation (r=0.98, p
