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Optimal Weights in Convex Functionals : Taking the Guesswork Out of Segmentation

Energy functional minimization is a popular technique for medical image segmentation. The segmentation must be initialized, weights for competing terms of an energy functional must be tuned, and the functional minimized. There is a substantial amount of guesswork involved. We reduce this guesswork by: first, analytically determining optimal weights; second, minimizing a convex energy functional independent of the initialization; and, third, providing an upper bound on the accuracy achievable by a given, convex energy functional. We demonstrate improved results over state of the art on a set of 470 clinical examples.