Multi-atlas label fusion with augmented atlases for fast and accurate segmentation of cardiac MR images

Abstract

Quantitative analysis of cardiac Magnetic Resonance (CMR) images requires accurate segmentation of myocardium. Although recent multi-atlas segmentation approaches have done a good job improving segmentation accuracy, they also increase the computational burden, which degrades their clinical utility. In this paper, we proposed a novel multi-atlas segmentation framework using an augmented atlas technique that is able to increase segmentation accuracy without increasing computational complexity. This is achieved by using roughly aligned neighborhood slices to improve patch-based label fusion accuracy. We evaluated the proposed approach on the MICCAI SATA Segmentation Challenge CAP dataset. Our results demonstrate that the proposed technique can achieve segmentation accuracy comparable to the state-of-the-art algorithms in much smaller amount of time.