Title:Segmenting and Tracking the Left Ventricle by Learning the Dynamics in Cardiac Images
Time: Friday 11:00-12:00
Location: 2222 Coover
Speaker: Wei Lu

Abstract:
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, approaching the problem of LV
segmentation as a recursive estimation problem is used.  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 is proposed to learn from training data
through an information-theoretic approach. To incorporate the learned
dynamic model into the segmentation framework and obtain predictions,
particle filtering is used. The framework uses a curve evolution
method to combine such predictions with the observed images to estimate
the LV boundaries at each time. This approach provides more accurate
segmentations than those from static image segmentation techniques, especially
when the observed data are of limited quality.