Doppler holography is an emerging retinal imaging technique that captures the dynamic behavior of blood flow with high temporal resolution, enabling quantitative assessment of retinal hemodynamics. This requires accurate segmentation of retinal arteries and veins, but traditional segmentation methods focus solely on spatial information and overlook the temporal richness of holographic data. In this work, we propose a simple yet effective approach for artery-vein segmentation in temporal Doppler holograms using standard segmentation architectures. By incorporating features derived from a dedicated pulse analysis pipeline, our method allows conventional U-Nets to exploit temporal dynamics and achieve performance comparable to more complex attention- or iteration-based models. These findings demonstrate that time-resolved preprocessing can unlock the full potential of deep learning for Doppler holography, opening new perspectives for quantitative exploration of retinal hemodynamics. The dataset is publicly available at https://huggingface.co/datasets/DigitalHolography/

翻译：多普勒全息术是一种新兴的视网膜成像技术，能以高时间分辨率捕捉血流的动态行为，从而实现对视网膜血流动力学的定量评估。这需要对视网膜动脉和静脉进行精确分割，但传统分割方法仅关注空间信息，忽略了全息数据的时间维度丰富性。本研究提出一种简单而有效的方法，利用标准分割架构对时序多普勒全息图进行动静脉分割。通过整合源自专用脉搏分析流程的特征提取模块，我们的方法使传统U-Net能够利用时序动态特性，并达到与更复杂的基于注意力或迭代的模型相当的性能。这些发现表明，时间分辨预处理能够充分释放深度学习在多普勒全息术中的潜力，为视网膜血流动力学的定量探索开辟了新视角。数据集已公开于https://huggingface.co/datasets/DigitalHolography/