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Medical imaging only indirectly measures the molecular identity of the tissuewithin each voxel, which often produces only ambiguous image evidence fortarget measures of interest, like semantic segmentation. This diversity and thevariations of plausible interpretations are often specific to given imageregions and may thus manifest on various scales, spanning all the way from thepixel to the image level. In order to learn a flexible distribution that canaccount for multiple scales of variations, we propose the HierarchicalProbabilistic U-Net, a segmentation network with a conditional variationalauto-encoder (cVAE) that uses a hierarchical latent space decomposition. Weshow that this model formulation enables sampling and reconstruction ofsegmenations with high fidelity, i.e. with finely resolved detail, whileproviding the flexibility to learn complex structured distributions acrossscales. We demonstrate these abilities on the task of segmenting ambiguousmedical scans as well as on instance segmentation of neurobiological andnatural images. Our model automatically separates independent factors acrossscales, an inductive bias that we deem beneficial in structured outputprediction tasks beyond segmentation.

A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities