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: this https URL

本文提出了一种基于非空间嵌入的无提案分割方法，可处理生物医学图像等难以密集标注的图像，并引入了一种自我监督一致性损失，以解决正 - 无标签训练中的挑战。在不同显微镜模式下的二维和三维分割问题以及城市景观和 CVPPP 实例分割基准上，实现了最先进的结果。