Section: New Results

Graph-based Segmentation

Paticipants: Sarah Parisot, Deepak Chittajallu, Ioannis Kakadiaris, Nikos Paragios

In [17] we revisited explicit contour-evolution segmentation methods driven from a graph-based shape prior. Prior knowledge through geometric constraints has been encoded to the model within pair-wise interactions between control points. The segmentation process was driven through an objective function seeking to move the control points towards image locations optimizing the expected visual properties of the organ while satisfying the prior geometric constraints being learned at training. In  [18] we have proposed a mathematical formalism for automatic tumor segmentation which was taking advantage of conventional segmentation likelihoods and atlas-based segmentation methods. The central idea was to jointly deform and segment an atlas such that the tumor likelihoods are maximized once projected to the targeted image while relaxing the registration constraints in this area. Furthermore we have endowed to this framework explicit estimation of uncertainties allowing the dynamic sampling of the graph structure resulting on significant speed up of the process while producing quantitative means for the interpretation of the final result.