Atrial fibrillation is a common heart arrhythmia, and can be effectively treated with ablation. Ablation planning
requires 3D models of the patient's left atrium (LA) and/or right atrium (RA), therefore an automatic segmentation
procedure to retrieve these models is desirable. In this study, we investigate the use of advanced level
set segmentation approaches to automatically segment RA in magnetic resonance angiographic (MRA) volume
images. Low contrast to noise ratio makes the boundary between the RA and the nearby structures nearly indistinguishable.
Therefore, pure data driven segmentation approaches such as watershed and ChanVese methods
are bound to fail. Incorporating training shapes through PCA modeling to constrain the segmentation is one
popular solution, and is also used in our segmentation framework. The shape parameters from PCA are optimized
with a global histogram based energy model. However, since the shape parameters span a much smaller
space, it can not capture fine details of the shape. Therefore, we employ a second refinement step after the shape
based segmentation stage, which follows closely the recent work of localized appearance model based techniques.
The local appearance model is established through a robust point tracking mechanism and is learned through
landmarks embedded on the surface of training shapes. The key contribution of our work is the combination
of a statistical shape prior and a localized appearance prior for level set segmentation of the right atrium from
MRA. We test this two step segmentation framework on porcine RA to verify the algorithm.