Abstract
The author describes some foundation of construction of statistical shape models. A statistical shape model is constructed from a set of training medical images. The first operation needed for the model construction is to make correspondences between the training images. It is required to make correspondences between the surfaces of organs of different patients when one represents organ regions in images with Point Distribution model. If one employs levels sets for the region representation, then one needs to normalize the body shapes in the images in order to make correspondences between voxels in the images. The author introduces five categories of the methods for the making of the correspondences. In addition, the author briefly describes the relationships between statistical shape models and deep neural networks.
