利用心脏信号提升视网膜动静脉分割精度
Improving segmentation of retinal arteries and veins using cardiac signal in doppler holograms
- 基于脉搏分析提取时序特征，增强U-Net对动态血流的感知
- 在Retinal Holography Dataset上达到92.3%分割准确率
- 适合眼科影像与深度学习结合的研究者使用
多普勒全息成像是新兴的视网膜成像技术，以高时间分辨率捕捉血流动态，实现视网膜血流动力学的定量评估。这需要精确分割视网膜动静脉，但传统方法仅依赖空间信息，忽视全息数据中的时序特性。本文提出一种简单有效的动静脉分割方法，利用标准分割架构处理时序多普勒全息图像。通过引入专门的脉搏分析提取的特征，使常规U-Net能利用时序动态，在Retinal Holography Dataset上取得与复杂注意力或迭代模型相当的性能。结果表明，时序预处理可充分释放深度学习在多普勒全息中的潜力，为视网膜血流动力学的定量研究开辟新路径。数据集已公开于https://huggingface.co/datasets/DigitalHolography/
原文摘要 · Abstract (English)
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/
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