  Microscopy image analysis often requires the segmentation of objects, but
training data for this task is typically scarce and hard to obtain. Here we
propose DenoiSeg, a new method that can be trained end-to-end on only a few
annotated ground truth segmentations. We achieve this by extending Noise2Void,
a self-supervised denoising scheme that can be trained on noisy images alone,
to also predict dense 3-class segmentations. The reason for the success of our
method is that segmentation can profit from denoising, especially when
performed jointly within the same network. The network becomes a denoising
expert by seeing all available raw data, while co-learning to segment, even if
only a few segmentation labels are available. This hypothesis is additionally
fueled by our observation that the best segmentation results on high quality
(very low noise) raw data are obtained when moderate amounts of synthetic noise
are added. This renders the denoising-task non-trivial and unleashes the
desired co-learning effect. We believe that DenoiSeg offers a viable way to
circumvent the tremendous hunger for high quality training data and effectively
enables few-shot learning of dense segmentations.
