Improving segmentation of retinal arteries and veins using cardiac signal in doppler holograms
This work addresses the need for improved retinal hemodynamics assessment in medical imaging, though it is incremental by enhancing existing methods with temporal preprocessing.
The paper tackled the problem of accurately segmenting retinal arteries and veins in Doppler holograms by incorporating temporal cardiac signal features into standard U-Net architectures, achieving performance comparable to more complex models.
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/