Unpaired Image-to-Image Translation for Segmentation and Signal Unmixing

• URL: http://arxiv.org/abs/2505.20746v1
• Date: Tue, 27 May 2025 05:36:50 GMT
• Title: Unpaired Image-to-Image Translation for Segmentation and Signal Unmixing
• Authors: Nikola Andrejic, Milica Spasic, Igor Mihajlovic, Petra Milosavljevic, Djordje Pavlovic, Filip Milisavljevic, Uros Milivojevic, Danilo Delibasic, Ivana Mikic, Sinisa Todorovic,
• Abstract summary: Ui2i is a novel model for unpaired image-to-image translation.<n>It is trained on content-wise unpaired datasets to enable style transfer across domains.
• Score: 12.51033401137503
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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