  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 and no large public data collections are available for
pre-training. 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 to instances and the overall pipeline can be trained
in a fully- or weakly supervised manner. We consider the challenging case of
positive-unlabeled supervision, where a novel self-supervised consistency loss
is introduced for the unlabeled parts of the training data. We evaluate the
proposed method on 2D and 3D segmentation problems in different microscopy
modalities as well as on the Cityscapes and CVPPP instance segmentation
benchmarks, achieving state-of-the-art results on the latter. The code is
available at: https://github.com/kreshuklab/spoco

Computer Vision and Pattern Recognition;Machine Learning