1. A method for automatically segmenting lung nodules in a three-dimensional (3D) Computed Tomography (CT) volume dataset, comprising the steps of: receiving an input corresponding to a user-selected point near a boundary of a nodule; constructing a model of the nodule from the user-selected point, the model being a deformable circle having a set of parameters &bgr;that represent a shape of the nodule; estimating continuous parts of the boundary and discontinuities of the boundary until the set of parameters &bgr; converges, using dynamic programming and Expectation Maximization (EM); and segmenting the nodule, based on estimates of the continuous parts of the boundary and the discontinuities of the boundary.