  Statistical shape analysis is a very useful tool in a wide range of medical
and biological applications. However, it typically relies on the ability to
produce a relatively small number of features that can capture the relevant
variability in a population. State-of-the-art methods for obtaining such
anatomical features rely on either extensive preprocessing or segmentation
and/or significant tuning and post-processing. These shortcomings limit the
widespread use of shape statistics. We propose that effective shape
representations should provide sufficient information to align/register images.
Using this assumption we propose a self-supervised, neural network approach for
automatically positioning and detecting landmarks in images that can be used
for subsequent analysis. The network discovers the landmarks corresponding to
anatomical shape features that promote good image registration in the context
of a particular class of transformations. In addition, we also propose a
regularization for the proposed network which allows for a uniform distribution
of these discovered landmarks. In this paper, we present a complete framework,
which only takes a set of input images and produces landmarks that are
immediately usable for statistical shape analysis. We evaluate the performance
on a phantom dataset as well as 2D and 3D images.
