SOM-GAN: A structure-preserving one-to-multiple generative adversarial network for unpaired medical image synthesis

GAN-based image translation has been widely used for cross-domain medical image synthesis. However, most existing methods follow a one-to-one mapping paradigm, requiring a separate model for each target domain and increasing the training cost in multi-target tasks such as multi-sequence MRI synthesis. Although recent one-to-multiple methods improve translation efficiency, they often struggle to preserve structural consistency because they lack explicit geometric constraints between the input and the synthesized images. Recent advances in segmentation foundation models have made anatomical annotations more accessible in medical imaging. However, existing image translation methods have not fully exploited the rich anatomical and structural priors provided by these annotations. As a result, the generated images may exhibit anatomical distortions and semantic misalignments, potentially limiting their reliability for anatomy-sensitive downstream tasks, such as organ and tumor segmentation in multi-sequence MRI. To address these limitations, we propose SOM-GAN, a Structure-preserving One-to-Multiple Generative Adversarial Network for efficient and structure-preserving image synthesis from a single source domain to multiple target domains. SOM-GAN formulates the translation process as a pair of three-player games for each target domain with a dual-path adversarial design. Specifically, a multi-domain appearance discriminator encourages realistic target-domain texture rendering, while a multi-domain structure discriminator introduces explicit structural constraints by adversarially aligning the joint distributions of images and semantic annotations. Through alternating optimization of these adversarial objectives, SOM-GAN enables the multi-domain conditional generator to synthesize high-quality images across multiple target domains. Extensive experiments on four typical yet challenging multi-target medical image translation tasks demonstrate that SOM-GAN outperforms state-of-the-art methods in both synthesized-image fidelity and downstream clinical segmentation and diagnostic classification tasks. The code is released at https://github.com/JinhaoLi98/SOM-GAN.