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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