1. A method for three-dimensional segmentation based on machine learning in a medical imaging system, the method comprising: scanning, by a medical scanner, a patient, the medical scanner comprising a magnetic resonance, computed tomography, x-ray, or ultrasound imaging system; loading, from memory, a medical dataset representing a three-dimensional region of the patient, the medical dataset being from the scanning by the medical scanner, the medical dataset comprising magnetic resonance, computed tomography, x-ray, or ultrasound data; applying, by a machine, the medical dataset to a multi-scale deep reinforcement machine-learnt model, the multi-scale deep reinforcement machine-learned model trained with multi-scale deep reinforcement learning to segment boundaries of a three-dimensional object from the medical dataset, the multi-scale deep reinforcement machine-learnt model including a machine-learnt policy of a sequence of actions for shape evolution over iterative refinements of the boundaries of the object, the machine-learnt policy trained to select each of the actions from possibilities given a state of fitting at each of the iterative refinements, the actions including actions in different resolutions of the medical dataset, the sequence resulting from the machine-learnt policy training based on maximizing rewards, the boundaries and refinements described using statistical shape-modeling, front propagation modeling, or voxel mask modeling, the multi-scale deep reinforcement machine-learnt model trained with natural evolution strategies to explore a parameter space based on the statistical shape-modeling, front propagation modeling, or voxel mask modeling; rendering, by a renderer, an image of the three-dimensional object based on the boundaries determined with the policy; and displaying the image on a display device.