Appearance-based modelling and segmentation of the hippocampus from MR images

Current segmentation techniques of the hippocampus from MR images generally require manual intervention or extensive computation time. Not all methods incorporate statistical information on the structure or volume of interest. This work is novel in that it presents a fully 3D, non-supervised appearance-based method for segmentation, hippocampus, based on a priori analysis of deformation fields. Early segmentation results demonstrate that this method is as accurate as ANIMAL, a non-linear registration and segmentation technique, while being faster. Refinements in the training strategy of the model should further improve accuracy with no additional on-line computational expense. A key feature of this approach is its ability to segment other structures of interest simply by retraining the model off-line on a new data set. The applicability of the proposed model towards shape deformation analysis is discussed.