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Morphological analysis of the left atrial appendage is an important tool toassess risk of ischemic stroke. Most deep learning approaches for 3Dsegmentation is guided by binary labelmaps, which results in voxelizedsegmentations unsuitable for morphological analysis. We propose to use signeddistance fields to guide a deep network towards morphologically consistent 3Dmodels. The proposed strategy is evaluated on a synthetic dataset of simplegeometries, as well as a set of cardiac computed tomography images containingthe left atrial appendage. The proposed method produces smooth surfaces with acloser resemblance to the true surface in terms of segmentation overlap andsurface distance.

Guiding 3D U-nets with signed distance fields for creating 3D models from images