Sectored Snakes: Evaluating Learned-Energy Segmentations

We describe how to teach deformable models to maximize image segmentation correctness based on user-specified criteria, and present a method for evaluating which criteria work best. We show how to evaluate the efficacy of any resulting deformable model, given a sampling of ground truth, a model of the range of shapes tried during optimization, and a measure of shape closeness. In the domain of abdominal CT images, we demonstrate such evaluation on a simple "sectoring" of a snake in which intensity and perpendicular gradient are observed over equal-length segments. This specific set of qualities shows a measured improvement over an objective function that is uniform around the shape, and it follows naturally from examination of the latter's failures due to image variations around the organ boundary.

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