text: The available methods include use of texture (Binder et al., 1999), local phase (Mulet-Parada and Noble, 2000), a gradient-based deformable model (Coppini et al., 1995), a region-based deformable model (Lin et al., 2003; Mignotte et al., 2001), and pixel-wise clustering (Boukerroui et al., 2003). Shape and time information have also been incorporated in an Active Appearance Motion Model (AAMM) (Bosch et al., 2002), an extended Kalman filter (Orderud et al., 2007), or a manifold learning-based framework (Yang et al., 2008). A more comprehensive overview is given in Noble et al. (2006). Compared with endocardial (ENDO) segmentation, the automation of epicardial (EPI) segmentation is inherently more challenging due to the severe imaging and anatomical factors.