  By their very nature microscopy images of cells and tissues consist of a
limited number of object types or components. In contrast to most natural
scenes, the composition is known a priori. Decomposing biological images into
semantically meaningful objects and layers is the aim of this paper. Building
on recent approaches to image de-noising we present a framework that achieves
state-of-the-art segmentation results requiring little or no manual
annotations. Here, synthetic images generated by adding cell crops are
sufficient to train the model. Extensive experiments on cellular images, a
histology data set, and small animal videos demonstrate that our approach
generalizes to a broad range of experimental settings. As the proposed
methodology does not require densely labelled training images and is capable of
resolving the partially overlapping objects it holds the promise of being of
use in a number of different applications.
