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Most state-of-the-art instance segmentation methods have to be trained ondensely annotated images. While difficult in general, this requirement isespecially daunting for biomedical images, where domain expertise is oftenrequired for annotation and no large public data collections are available forpre-training. We propose to address the dense annotation bottleneck byintroducing a proposal-free segmentation approach based on non-spatialembeddings, which exploits the structure of the learned embedding space toextract individual instances in a differentiable way. The segmentation loss canthen be applied directly to instances and the overall pipeline can be trainedin a fully- or weakly supervised manner. We consider the challenging case ofpositive-unlabeled supervision, where a novel self-supervised consistency lossis introduced for the unlabeled parts of the training data. We evaluate theproposed method on 2D and 3D segmentation problems in different microscopymodalities as well as on the Cityscapes and CVPPP instance segmentationbenchmarks, achieving state-of-the-art results on the latter. The code isavailable at: https://github.com/kreshuklab/spoco

Sparse Object-level Supervision for Instance Segmentation with Pixel Embeddings