Using atlas prior with graph cut methods for right ventricle segmentation from cardiac MRI

Abstract

Right ventricle segmentation helps quantify many functional parameters of the heart and construct anatomical models for intervention planning. Here we propose a fast and accurate graph cut segmentation algorithm to extract the right ventricle from cine cardiac MRI sequences. A shape prior obtained by propagating the right ventricle label from an average atlas via affine registration is incorporated into the graph energy. The optimal segmentation obtained from the graph cut is iteratively refined to produce the final right ventricle blood pool segmentation. We evaluate our segmentation results against gold-standard expert manual segmentation of 16 cine MRI datasets available through the MICCAI 2012 Cardiac MR Right Ventricle Segmentation Challenge. Our method achieved an average Dice Index 0.83, a Jaccard Index 0.75, Mean absolute distance of 5.50 mm, and a Hausdorff distance of 10.00 mm.