  This paper introduces a network for volumetric segmentation that learns from
sparsely annotated volumetric images. We outline two attractive use cases of
this method: (1) In a semi-automated setup, the user annotates some slices in
the volume to be segmented. The network learns from these sparse annotations
and provides a dense 3D segmentation. (2) In a fully-automated setup, we assume
that a representative, sparsely annotated training set exists. Trained on this
data set, the network densely segments new volumetric images. The proposed
network extends the previous u-net architecture from Ronneberger et al. by
replacing all 2D operations with their 3D counterparts. The implementation
performs on-the-fly elastic deformations for efficient data augmentation during
training. It is trained end-to-end from scratch, i.e., no pre-trained network
is required. We test the performance of the proposed method on a complex,
highly variable 3D structure, the Xenopus kidney, and achieve good results for
both use cases.
