Segmentation of the left myocardium in four-dimensional (space-time)
cardiac MRI data sets is a prerequisite of many diagnostic tasks.
We propose a fully automatic method based on global minimization of an
energy functional by means of the graphcut algorithm.
Starting from automatically obtained segmentations of the left and
right ventricles and a cardiac region of interest, a spatial model is
constructed using simple and plausible assumptions.
This model is used to learn the appearance of different tissue types
by non parametric robust estimation.
Our method does not require previously trained shape or appearance
models. Processing takes 30-40s on current hardware.
We evaluated our method on 11 clinical cardiac MRI data sets acquired
using cine balanced fast field echo. Linear regression of the
automatically segmented myocardium volume against manual segmentations
(performed by a radiologist) showed an RMS error of about 12ml.