  Automated image processing approaches are indispensable for many biomedical
experiments and help to cope with the increasing amount of microscopy image
data in a fast and reproducible way. Especially state-of-the-art deep
learning-based approaches most often require large amounts of annotated
training data to produce accurate and generalist outputs, but they are often
compromised by the general lack of those annotated data sets. In this work, we
propose how conditional generative adversarial networks can be utilized to
generate realistic image data for 3D fluorescence microscopy from annotation
masks of 3D cellular structures. In combination with mask simulation
approaches, we demonstrate the generation of fully-annotated 3D microscopy data
sets that we make publicly available for training or benchmarking. An
additional positional conditioning of the cellular structures enables the
reconstruction of position-dependent intensity characteristics and allows to
generate image data of different quality levels. A patch-wise working principle
and a subsequent full-size reassemble strategy is used to generate image data
of arbitrary size and different organisms. We present this as a
proof-of-concept for the automated generation of fully-annotated training data
sets requiring only a minimum of manual interaction to alleviate the need of
manual annotations.
