Segmenting and Tracking the Left Ventricle by Learning the Dynamics in Cardiac Images

Having accurate left ventricle (LV) segmentations across a cardiac cycle provides useful quantitative (e.g. ejection fraction) and qualitative information for diagnosis of certain heart conditions. Existing LV segmentation techniques are founded mostly upon algorithms for segmenting static images. In order to exploit the dynamic structure of the heart in a principled manner, we approach the problem of LV segmentation as a recursive estimation problem. In our framework, LV boundaries constitute the dynamic system state to be estimated, and a sequence of observed cardiac images constitute the data. By formulating the problem as one of state estimation, the segmentation at each particular time is based not only on the data observed at that instant, but also on predictions based on past segmentations. This requires a dynamical system model of the LV, which we propose to learn from training data through an information-theoretic approach. To incorporate the learned dynamic model into our seg...

• Type: Conference
• Year: 2005
• Where: IPMI
• Authors: Alan S. Willsky, Godtfred Holmvang, Müjdat Çetin, Raymond Chan, Venkat Chandar, Vivek Reddy, Walter Sun
• Keywords: IPMI 2005, LV Boundaries, LV Segmentation Techniques, Medical Imaging, Observed Cardiac Images