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