The choroid is a key vascular layer of the eye, supplying oxygen to the retinal photoreceptors. Non-invasive enhanced depth imaging optical coherence tomography (EDI-OCT) has recently improved access and visualisation of the choroid, making it an exciting frontier for discovering novel vascular biomarkers in ophthalmology and wider systemic health. However, current methods to measure the choroid often require use of multiple, independent semi-automatic and deep learning-based algorithms which are not made open-source. Previously, Choroidalyzer -- an open-source, fully automatic deep learning method trained on 5,600 OCT B-scans from 385 eyes -- was developed to fully segment and quantify the choroid in EDI-OCT images, thus addressing these issues. Using the same dataset, we propose a Robust, Resolution-agnostic and Efficient Attention-based network for CHoroid segmentation (REACH). REACHNet leverages multi-resolution training with domain-specific data augmentation to promote generalisation, and uses a lightweight architecture with resolution-agnostic self-attention which is not only faster than Choroidalyzer's previous network (4 images/s vs. 2.75 images/s on a standard laptop CPU), but has greater performance for segmenting the choroid region, vessels and fovea (Dice coefficient for region 0.9769 vs. 0.9749, vessels 0.8612 vs. 0.8192 and fovea 0.8243 vs. 0.3783) due to its improved hyperparameter configuration and model training pipeline. REACHNet can be used with Choroidalyzer as a drop-in replacement for the original model and will be made available upon publication.

翻译：脉络膜是眼球的关键血管层，为视网膜光感受器供氧。近年来，非侵入性增强深度成像光学相干断层扫描（EDI-OCT）技术显著提升了脉络膜的成像可及性与可视化效果，使其成为眼科学及更广泛全身健康领域中探索新型血管生物标志物的前沿热点。然而，当前测量脉络膜的方法通常需要依赖多个独立、未开源且基于半自动或深度学习的算法。此前，研究者开发了Choroidalyzer——一种基于5,600张来自385只眼睛的OCT B扫描图像训练的开源全自动深度学习方法，用于对EDI-OCT图像中的脉络膜进行完整分割与量化，从而解决了上述问题。基于同一数据集，我们提出了一种鲁棒、分辨率无关且高效的基于注意力的脉络膜分割网络（REACH）。REACHNet利用多分辨率训练与面向特定领域的数据增强策略以提升泛化能力，并采用轻量级架构与分辨率无关的自注意力机制，不仅比Choroidalyzer原有网络速度更快（在标准笔记本电脑CPU上处理速度达4图像/秒 vs. 2.75图像/秒），且因其改进的超参数配置与模型训练流程，在脉络膜区域、血管及中央凹的分割性能上表现更优（区域Dice系数0.9769 vs. 0.9749，血管0.8612 vs. 0.8192，中央凹0.8243 vs. 0.3783）。REACHNet可作为Choroidalyzer中原始模型的即插即用替代方案，并将在论文发表时开源提供。