1 research outputs found
Mixture Modeling of Global Shape Priors and Autoencoding Local Intensity Priors for Left Atrium Segmentation
Difficult image segmentation problems, for instance left atrium MRI, can be
addressed by incorporating shape priors to find solutions that are consistent
with known objects. Nonetheless, a single multivariate Gaussian is not an
adequate model in cases with significant nonlinear shape variation or where the
prior distribution is multimodal. Nonparametric density estimation is more
general, but has a ravenous appetite for training samples and poses serious
challenges in optimization, especially in high dimensional spaces. Here, we
propose a maximum-a-posteriori formulation that relies on a generative image
model by incorporating both local intensity and global shape priors. We use
deep autoencoders to capture the complex intensity distribution while avoiding
the careful selection of hand-crafted features. We formulate the shape prior as
a mixture of Gaussians and learn the corresponding parameters in a
high-dimensional shape space rather than pre-projecting onto a low-dimensional
subspace. In segmentation, we treat the identity of the mixture component as a
latent variable and marginalize it within a generalized
expectation-maximization framework. We present a conditional maximization-based
scheme that alternates between a closed-form solution for component-specific
shape parameters that provides a global update-based optimization strategy, and
an intensity-based energy minimization that translates the global notion of a
nonlinear shape prior into a set of local penalties. We demonstrate our
approach on the left atrial segmentation from gadolinium-enhanced MRI, which is
useful in quantifying the atrial geometry in patients with atrial fibrillation.Comment: Statistical Atlases and Computational Models of the Heart. Atrial
Segmentation and LV Quantification Challenges 201