Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI

Abstract

We present a framework for the analysis of short axis cardiac MRI, using statistical models of shape and appearance. The framework integrates temporal and structural constraints and avoids common optimization problems inherent in such high dimensional models. The first contribution is the introduction of an algorithm for fitting 3D active appearance models (AAMs) on short axis cardiac MRI. We observe a 44-fold increase in fitting speed and a segmentation accuracy that is on par with Gauss-Newton optimization, one of the most widely used optimization algorithms for such problems. The second contribution involves an investigation on hierarchical 2D+time active shape models (ASMs), that integrate temporal constraints and simultaneously improve the 3D AAM based segmentation. We obtain encouraging results (endocardial/epicardial error 1.43+/-0.49 mm/1.51+/-0.48 mm) on 7980 short axis cardiac MR images acquired from 33 subjects. We have placed our dataset online, for the community to use and build upon.