  Statistical shape modeling (SSM) directly from 3D medical images is an
underutilized tool for detecting pathology, diagnosing disease, and conducting
population-level morphology analysis. Deep learning frameworks have increased
the feasibility of adopting SSM in medical practice by reducing the
expert-driven manual and computational overhead in traditional SSM workflows.
However, translating such frameworks to clinical practice requires calibrated
uncertainty measures as neural networks can produce over-confident predictions
that cannot be trusted in sensitive clinical decision-making. Existing
techniques for predicting shape with aleatoric (data-dependent) uncertainty
utilize a principal component analysis (PCA) based shape representation
computed in isolation from the model training. This constraint restricts the
learning task to solely estimating pre-defined shape descriptors from 3D images
and imposes a linear relationship between this shape representation and the
output (i.e., shape) space. In this paper, we propose a principled framework
based on the variational information bottleneck theory to relax these
assumptions while predicting probabilistic shapes of anatomy directly from
images without supervised encoding of shape descriptors. Here, the latent
representation is learned in the context of the learning task, resulting in a
more scalable, flexible model that better captures data non-linearity.
Additionally, this model is self-regularized and generalizes better given
limited training data. Our experiments demonstrate that the proposed method
provides improved accuracy and better calibrated aleatoric uncertainty
estimates than state-of-the-art methods.
