1. A method for training a region clustering forest, comprising: receiving a set of medical training images for a population of patients; extracting a set of image patches from each image in the set of medical training images; and generating a plurality of region clustering trees each minimizing a loss function based on respective randomly selected subsets of the set of image patches to train the region clustering forest, wherein each of the plurality of region clustering trees cluster image patches at a plurality of leaf nodes and the loss function measures a compactness of the cluster of image patches at each leaf node in each of the plurality of region clustering trees.