  A computational anatomy atlas is a set of internal organ geometries. It is
based on data of real patients and complemented with virtual cases by using a
some numerical approach. Atlases are in demand in computational physiology,
especially in cardiological and neurophysiological applications. Usually, atlas
generation uses explicit object representation, such as voxel models or surface
meshes. In this paper, we propose a method of atlas generation using an
implicit representation of 3D objects. Our approach has two key stages. The
first stage converts voxel models of segmented organs to implicit form using
the usual multilayer perceptron. This stage smooths the model and reduces
memory consumption. The second stage uses a multilayer perceptron with
Lipschitz regularization. This neural network provides a smooth transition
between implicitly defined 3D geometries. Our work shows examples of models of
the left and right human ventricles. All code and data for this work are open.
