  Most state-of-the-art instance segmentation methods have to be trained on
densely annotated images. While difficult in general, this requirement is
especially daunting for biomedical images, where domain expertise is often
required for annotation. We propose to address the dense annotation bottleneck
by introducing a proposal-free segmentation approach based on non-spatial
embeddings, which exploits the structure of the learned embedding space to
extract individual instances in a differentiable way. The segmentation loss can
then be applied directly on the instances and the overall method can be trained
on ground truth images where only a few objects are annotated, from scratch or
in a semi-supervised transfer learning setting. In addition to the segmentation
loss, our setup allows to apply self-supervised consistency losses on the
unlabeled parts of the training data. We evaluate the proposed method on
challenging 2D and 3D segmentation problems in different microscopy modalities
as well as on the popular CVPPP instance segmentation benchmark where we
achieve state-of-the-art results.
  The code is available at: https://github.com/kreshuklab/spoco
