Zhilin Zou

Unpaired medical image translation previously packed structure preservation and appearance generation into one source-to-target mapping, which without paired supervision can drop vessels or distort the foveal avascular zone. This work separates the two: a cross-modality shared structure latent space is learned first, then the structure encoder is frozen and a conditional flow model renders only target-modality appearance. On OCT-to-OCTA and brain MR-to-CT, it outperforms all unpaired baselines and, despite no paired supervision, surpasses paired baselines, improving PSNR by up to 11.2% and SSIM by up to 26.4% on OCT-to-OCTA.

Unpaired medical image translation previously packed structure preservation and appearance generation into one source-to-target mapping, which without paired supervision can drop vessels or distort the foveal avascular zone. This work separates the two: a cross-modality shared structure latent space is learned first, then the structure encoder is frozen and a conditional flow model renders only target-modality appearance. On OCT-to-OCTA and brain MR-to-CT, it outperforms all unpaired baselines and, despite no paired supervision, surpasses paired baselines, improving PSNR by up to 11.2% and SSIM by up to 26.4% on OCT-to-OCTA.