3D appearance model for hippocampus segmentation from MRI

A generic approach to building a 3D active appearance model (AAM) for medical image segmentation is presented. Provided a training set of manually segmented images, the model-building procedure is fully automatic. Shape information is obtained from a free-form image registration algorithm. Our AAM is evaluated using the hippocampus as the structure of interest (SOI); the training set consists of 28 manually segmented brain MR scans. The main contributions of this work are: (a) A concept of incorporating the SOI surroundings into the AAM. The concept is also applicable to other medical image based AAMs. (b) A two-step free-form registration procedure (matching the grayscale images first, then matching the segmented images). In this manner, landmark correspondence is improved, and the expert knowledge (i.e., manual segmentations) is less compromised by registration inaccuracies, (c) Two distinct AAM versions are compared: one without and one with statistical texture variation information. Compared to segmentation of an unknown image by registration to a reference image, the main advantage of the AAM is speed: the computation time is brought down from around 5 hours (for free-form deformation computation) to only a few minutes (for optimizing the model parameters), with only a slight degradation in segmentation accuracy.