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This work introduces a new proposal-free instance segmentation method thatbuilds on single-instance segmentation masks predicted across the entire imagein a sliding window style. In contrast to related approaches, our methodconcurrently predicts all masks, one for each pixel, and thus resolves anyconflict jointly across the entire image. Specifically, predictions fromoverlapping masks are combined into edge weights of a signed graph that issubsequently partitioned to obtain all final instances concurrently. The resultis a parameter-free method that is strongly robust to noise and prioritizespredictions with the highest consensus across overlapping masks. All masks aredecoded from a low dimensional latent representation, which results in greatmemory savings strictly required for applications to large volumetric images.We test our method on the challenging CREMI 2016 neuron segmentation benchmarkwhere it achieves competitive scores.

Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks