Diabetic Retinopathy (DR), an ocular complication of diabetes, is a leading cause of blindness worldwide. Traditionally, DR is monitored using Color Fundus Photography (CFP), a widespread 2-D imaging modality. However, DR classifications based on CFP have poor predictive power, resulting in suboptimal DR management. Optical Coherence Tomography Angiography (OCTA) is a recent 3-D imaging modality offering enhanced structural and functional information (blood flow) with a wider field of view. This paper investigates automatic DR severity assessment using 3-D OCTA. A straightforward solution to this task is a 3-D neural network classifier. However, 3-D architectures have numerous parameters and typically require many training samples. A lighter solution consists in using 2-D neural network classifiers processing 2-D en-face (or frontal) projections and/or 2-D cross-sectional slices. Such an approach mimics the way ophthalmologists analyze OCTA acquisitions: 1) en-face flow maps are often used to detect avascular zones and neovascularization, and 2) cross-sectional slices are commonly analyzed to detect macular edemas, for instance. However, arbitrary data reduction or selection might result in information loss. Two complementary strategies are thus proposed to optimally summarize OCTA volumes with 2-D images: 1) a parametric en-face projection optimized through deep learning and 2) a cross-sectional slice selection process controlled through gradient-based attribution. The full summarization and DR classification pipeline is trained from end to end. The automatic 2-D summary can be displayed in a viewer or printed in a report to support the decision. We show that the proposed 2-D summarization and classification pipeline outperforms direct 3-D classification with the advantage of improved interpretability.

翻译：糖尿病视网膜病变（DR）是糖尿病的眼部并发症，是全球致盲的主要原因之一。传统上，DR 的监测依赖于彩色眼底照相（CFP）这一广泛应用的二维成像模态。然而，基于 CFP 的 DR 分类预测能力较差，导致 DR 管理效果欠佳。光学相干断层扫描血管造影（OCTA）是一种新兴的三维成像模态，可提供增强的结构与功能信息（血流信号），并具有更宽的视野。本文探讨了利用三维 OCTA 进行自动 DR 严重程度评估的方法。该任务的一个直接方案是采用三维神经网络分类器。然而，三维架构参数众多，通常需要大量训练样本。一种更轻量的方案是使用二维神经网络分类器处理二维正面（即冠状面）投影和/或二维横截面切片。这种方法模拟了眼科医生分析 OCTA 图像的过程：1) 正面血流图常用于检测无灌注区与新生血管；2) 横截面切片则常用于分析黄斑水肿等情况。然而，任意的数据降维或选择可能导致信息丢失。为此，本文提出了两种互补策略以优化地将 OCTA 体数据摘要为二维图像：1) 通过深度学习优化的参数化正面投影，以及2) 通过基于梯度的归因控制横截面切片的选择。整个摘要生成与 DR 分类流程采用端到端训练。自动生成的二维摘要可显示在阅片器中或打印在报告中以辅助决策。我们证明，所提出的二维摘要与分类流程在优于直接三维分类的同时，还具有更强的可解释性优势。