In the present paper we describe the automatic construction of a statistical shape model of the whole heart built
from a training set of 100 Multi-Slice Computed Tomography (MSCT) studies of pathologic and asymptomatic
patients, including 15 (temporal) cardiac phases each. With these data sets we were able to build a compact
and representative shape model of both inter-subject and temporal variability. A practical limitation in building
statistical shape models, and in particular point distribution models (PDM), is the manual delineation of the
training set. A key advantage of the proposed method is to overcome this limitation by not requiring them.
Another one is the use of MSCT images, which thanks to their excellent anatomical depiction, have allowed
for a realistic heart representation, including the four chambers and connected vasculature. The generalization
ability of the shape model permits its deformation to unseen anatomies with an acceptable accuracy. Moreover,
its compactness allows for having a reduced set of parameters to describe the modeled population. By varying
these parameters, the statistical model can generate a set of valid examples. This is especially useful for the
generation of synthetic populations of cardiac shapes, that may correspond e.g. to healthy or diseased cases.
Finally, an illustrative example of the use of the constructed shape model for cardiac segmentation is provided.