  Morphological analysis of the left atrial appendage is an important tool to
assess risk of ischemic stroke. Most deep learning approaches for 3D
segmentation is guided by binary labelmaps, which results in voxelized
segmentations unsuitable for morphological analysis. We propose to use signed
distance fields to guide a deep network towards morphologically consistent 3D
models. The proposed strategy is evaluated on a synthetic dataset of simple
geometries, as well as a set of cardiac computed tomography images containing
the left atrial appendage. The proposed method produces smooth surfaces with a
closer resemblance to the true surface in terms of segmentation overlap and
surface distance.
