1. A method for segmenting organs in digitized medical images, the method comprising: using a computer to perform steps comprising: providing a set of training images of an object of interest, wherein said object of interest has been segmented; computing a surface mesh having a plurality of mesh cells that approximates a border of said object of interest; extracting positive examples of all mesh cells and extracting negative examples in the neighborhood of each mesh cell which do not belong to the object surface; training from said positive examples and negative examples a plurality of classifiers for outputting a probability of a point being a center of a particular mesh cell; computing an active shape model using a robust subset of center points in said mesh cells; wherein said robust subset of said mesh cells is determined by maximizing arg ⁢ max w ⁡ ( α ⁢ ∑ i n ⁢ ⁢ ∑ j m ⁢ ⁢ w i ⁢ g ij + β ⁢ ∑ i n ⁢ ⁢ w i ⁢ C _ i ) , g ij <D, wherein g ij is a geodesic distance between centers of mesh cells i and j, D is a maximum distance between mesh cells, C i is an average classification accuracy of said classifier for mesh cell i, α and β are adjustable parameters that can trade-off between sparsity and classification accuracy, and w i is a resulting weight of mesh cell i; generating a new shape by iteratively deforming said active shape model to fit a test image; and using said classifiers to calculate a probability of each center point of said new shape being a center of a particular mesh cell which said classifier was trained to recognize.