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Abstract #3758

Creating 3D Heart Models of Children with Congenital Heart Disease using Magnetic Resonance Imaging

Danielle F. Pace 1 , Polina Golland 1 , David Annese 2 , Tal Geva 2,3 , Andrew J. Powell 2,3 , and Mehdi H. Moghari 2,3

1 Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States, 2 Department of Cardiology, Boston Children's Hospital, Boston, MA, United States, 3 Department of Pediatrics, Harvard Medical School, Boston, MA, United States

We present a semi-automatic segmentation algorithm to create 3D heart models of children with complex congenital heart disease from 3D magnetic resonance images, which have promise for planning interventions. After 10-15 short-axis slices are segmented manually (in less than one hour of interaction time), a patch-based algorithm segments the remaining slices automatically. 3D surface models are then generated from the segmented blood pool and epicardium. The semi-automatic algorithm was evaluated using images acquired from 4 patients. Compared to manual segmentation, the proposed algorithm had surface-to-surface distance errors of 0.51 +/- 0.90 mm (blood pool) and 0.60 +/- 0.99 mm (epicardium).

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