Improving segmentation of retinal arteries and veins using cardiac signal in doppler holograms

TL;DR

This paper introduces a method that enhances retinal artery-vein segmentation in Doppler holography by integrating temporal blood flow features, enabling standard deep learning models to leverage dynamic information for improved accuracy.

Contribution

It presents a simple approach to incorporate temporal features into conventional segmentation architectures, improving performance without complex model modifications.

Findings

Temporal features improve segmentation accuracy.

Method achieves comparable results to complex models.

Public dataset availability supports further research.

Abstract

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/