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Sectored snakes: evaluating learned-energy segmentations

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2 Author(s)
Fenster, S.D. ; Dept. of Comput. Sci., City Coll. of New York, NY, USA ; Kender, J.R.

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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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:23 ,  Issue: 9 )