An ideal segmentation algorithm could be applied equally to the problem of isolating organs in a medical volume or to editing a digital photograph without modifying the algorithm, changing parameters, or sacrificing segmentation quality. However, a general-purpose, multilabel segmentation of objects in an image/volume remains a challenging problem. In this talk, I will describe a recently developed approach to this problem that inputs a few training points from a user (e.g., from mouse clicks) and produces a segmentation by computing the probabilities that a random walker leaving unlabeled pixels/voxels will first strike the training set. By exact mathematical equivalence with a problem from potential theory, these probabilities may be computed analytically and deterministically. The algorithm is developed on an arbitrary, weighted, graph in order to maximize the broadness of application. I will illustrate the use of this approach with examples from several segmentation problems (without modifying the algorithm or the single free parameter), compare the behavior of the algorithm to other approaches and discuss the theoretical properties that describe its behavior. Time permitting, new results on a solution to the combinatorial Plateau problem on a weighted complex will also be given, with application to 3D image segmentation. More information on this research may be found online at: http://www.cns.bu.edu/~lgrady/