2 research outputs found
Segmentation of the evolving left ventricle by learning the dynamics
We propose a method for recursive segmentation of the left ventricle
(LV) across a temporal sequence of magnetic resonance (MR) images.
The approach involves a technique for learning the LV boundary
dynamics together with a particle-based inference algorithm on
a loopy graphical model capturing the temporal periodicity of the
heart. The dynamic system state is a low-dimensional representation
of the boundary, and boundary estimation involves incorporating
curve evolution into state estimation. 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 and future boundary estimates. We assess
and demonstrate the effectiveness of the proposed framework
on a large data set of breath-hold cardiac MR image sequences