Aorta Segmentation in Axial Cardiac Cine MRI via Graphical Models

We propose an automatic approach to aorta segmentation in axial cardiac cine MRI. The segmentation task is formulated as a probabilistic inference problem, seeking for the most probable constellation of aorta locations and shapes in time. To this end, a graphical model is developed that implements the mutual dependencies of the aorta parameters along the cine sequence. Our approach integrates effective means of manual guidance for post-correction in case of erroneous results, requiring only user interaction where necessary. Experiments on a data set of 20 cine sequences showed average Dice coefficients close to the interreader variability while outperforming previous work in the field. Only two post-corrections were required for the entire data set. Results also indicate high stability of our approach w.r.t. re-parameterization.