Multi-Atlas Segmentation of the Cardiac MR Right Ventricle

As an entry to the MICCAI 2012 Cardiac MR Right Ventricle Segmentation Challenge, this paper presents a multi-atlas-based automatic pipeline for segmenting the right ventricle in MR images. Multiatlas segmentation relies on two major components: image registration to propagate segmentation labels into target image that needs to be segmented, and label fusion to effectively combine those labels from multiple atlases into final segmentation. In the challenge dataset, we observe different imaging fields-of-view (FOVs), different structures around cardiac structures, and as such, registration and label fusion become quite difficult. We propose to drive both components by an attribute-based similarity metric and a mutual-saliency-based reliability metric. The fundamental idea is to improve registration and label fusion by looking for corresponding voxels that are similar (as measured by their Gabor attributes in the neighborhood), and more importantly, reliably similar (as measured by the mutual-saliency of their matching) between atlas and target images.