Focus on Content not Noise: Improving Image Generation for Nuclei Segmentation by Suppressing Steganography in CycleGAN

TL;DR

This paper enhances CycleGAN for nuclei image synthesis by removing hidden steganography using DCT filtering, resulting in more coherent images and improved segmentation performance.

Contribution

It introduces a DCT-based low pass filtering method to eliminate steganography in CycleGAN-generated images, improving content fidelity for nuclei segmentation.

Findings

5.4 percentage point increase in F1-score with the proposed method

Enhanced coherence between generated images and masks

Potential for better synthetic dataset quality in microscopy

Abstract

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.