  Surface reconstruction is a vital tool in a wide range of areas of medical
image analysis and clinical research. Despite the fact that many methods have
proposed solutions to the reconstruction problem, most, due to their
deterministic nature, do not directly address the issue of quantifying
uncertainty associated with their predictions. We remedy this by proposing a
novel probabilistic deep learning approach capable of simultaneous surface
reconstruction and associated uncertainty prediction. The method incorporates
prior shape information in the form of a principal component analysis (PCA)
model. Experiments using the UK Biobank data show that our probabilistic
approach outperforms an analogous deterministic PCA-based method in the task of
2D organ delineation and quantifies uncertainty by formulating distributions
over predicted surface vertex positions.
