Kavli Affiliate: Yi Zhou
| First 5 Authors: Zihao Chen, Zihao Chen, , ,
| Summary:
Unpaired image-to-image translation has emerged as a crucial technique in
medical imaging, enabling cross-modality synthesis, domain adaptation, and data
augmentation without costly paired datasets. Yet, existing approaches often
distort fine curvilinear structures, such as microvasculature, undermining both
diagnostic reliability and quantitative analysis. This limitation is
consequential in ophthalmic and vascular imaging, where subtle morphological
changes carry significant clinical meaning. We propose Curvilinear
Structure-preserving Translation (CST), a general framework that explicitly
preserves fine curvilinear structures during unpaired translation by
integrating structure consistency into the training. Specifically, CST augments
baseline models with a curvilinear extraction module for topological
supervision. It can be seamlessly incorporated into existing methods. We
integrate it into CycleGAN and UNSB as two representative backbones.
Comprehensive evaluation across three imaging modalities: optical coherence
tomography angiography, color fundus and X-ray coronary angiography
demonstrates that CST improves translation fidelity and achieves
state-of-the-art performance. By reinforcing geometric integrity in learned
mappings, CST establishes a principled pathway toward curvilinear
structure-aware cross-domain translation in medical imaging.
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