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.

翻译：无配对图像到图像翻译已成为医学成像中的关键技术，它能够在无需昂贵配对数据集的情况下实现跨模态合成、域适应和数据增强。然而，现有方法常常会扭曲精细的曲线结构（如微血管系统），从而损害诊断可靠性和定量分析。这一局限在眼科和血管成像中影响重大，因为细微的形态变化具有重要的临床意义。我们提出了曲线结构保持翻译（CST），这是一个通用框架，通过将结构一致性整合到训练中，在无配对翻译过程中显式地保留精细曲线结构。具体而言，CST通过一个用于拓扑监督的曲线提取模块来增强基线模型。它可以无缝集成到现有方法中。我们将其集成到CycleGAN和UNSB这两个代表性骨干网络中。在光学相干断层扫描血管成像、彩色眼底和X射线冠状动脉血管造影这三种成像模态上的综合评估表明，CST提高了翻译保真度，并实现了最先进的性能。通过增强学习映射中的几何完整性，CST为医学成像中实现曲线结构感知的跨域翻译建立了一条原则性路径。