Learning continuous shape priors from sparse data with neural implicit functions

Tamaz Amiranashvili, David Lüdke, Hongwei Bran Li, Stefan Zachow, Bjoern H. Menze

Abstract: Highlights•A novel continuous shape model enables training on sparse, anisotropic volumetric segmentation masks.•High-resolution shape reconstruction from various sparse inputs.•Unification of sparse segmentations in a meaningful, low-dimensional joint latent space.•Discriminative latent space allows for the classification of knee osteoarthritis from sparse inputs.