Focus on Content not Noise: Improving Image Generation for Nuclei
  Segmentation by Suppressing Steganography in CycleGAN
Jonas Utz Tobias Weise Maja Schlereth Fabian Wagner Mareike Thies Mingxuan Gu Stefan Uderhardt Katharina Breininger
Annotating nuclei in microscopy images for the training of neural networks is
a laborious task that requires expert knowledge and suffers from inter- and
intra-rater variability, especially in fluorescence microscopy. Generative
networks such as CycleGAN can inverse the process and generate synthetic
microscopy images for a given mask, thereby building a synthetic dataset.
However, past works report content inconsistencies between the mask and
generated image, partially due to CycleGAN minimizing its loss by hiding
shortcut information for the image reconstruction in high frequencies rather
than encoding the desired image content and learning the target task. In this
work, we propose to remove the hidden shortcut information, called
steganography, from generated images by employing a low pass filtering based on
the DCT. We show that this increases coherence between generated images and
cycled masks and evaluate synthetic datasets on a downstream nuclei
segmentation task. Here we achieve an improvement of 5.4 percentage points in
the F1-score compared to a vanilla CycleGAN. Integrating advanced
regularization techniques into the CycleGAN architecture may help mitigate
steganography-related issues and produce more accurate synthetic datasets for
nuclei segmentation.