Unpaired Image-to-Image Translation for Segmentation and Signal Unmixing
Journal:
arXiv
Published Date:
May 27, 2025
Abstract
This work introduces Ui2i, a novel model for unpaired image-to-image
translation, trained on content-wise unpaired datasets to enable style transfer
across domains while preserving content. Building on CycleGAN, Ui2i
incorporates key modifications to better disentangle content and style
features, and preserve content integrity. Specifically, Ui2i employs
U-Net-based generators with skip connections to propagate localized shallow
features deep into the generator. Ui2i removes feature-based normalization
layers from all modules and replaces them with approximate bidirectional
spectral normalization -- a parameter-based alternative that enhances training
stability. To further support content preservation, channel and spatial
attention mechanisms are integrated into the generators. Training is
facilitated through image scale augmentation. Evaluation on two biomedical
tasks -- domain adaptation for nuclear segmentation in immunohistochemistry
(IHC) images and unmixing of biological structures superimposed in
single-channel immunofluorescence (IF) images -- demonstrates Ui2i's ability to
preserve content fidelity in settings that demand more accurate structural
preservation than typical translation tasks. To the best of our knowledge, Ui2i
is the first approach capable of separating superimposed signals in IF images
using real, unpaired training data.
