DISCOVER：用于自动糖尿病视网膜病变诊断的光学相干断层扫描血管造影的二维多视图摘要
DISCOVER: 2-D Multiview Summarization of Optical Coherence Tomography Angiography for Automatic Diabetic Retinopathy Diagnosis
AI总结：
本文提出基于OCTA的二维多视图摘要方法（DISCOVER），通过深度学习优化的正面投影和梯度归因切片选择，实现端到端的DR自动诊断，优于三维分类且更具可解释性。
摘要：
糖尿病视网膜病变（DR）是糖尿病的眼部并发症，是全球失明的主要原因。传统上，DR通过彩色眼底摄影（CFP）进行监测，这是一种广泛使用的二维成像方式。然而，基于CFP的DR分类预测能力较差，导致DR管理效果不佳。光学相干断层扫描血管造影（OCTA）是一种新兴的三维成像方式，提供增强的结构和功能信息（血流），并具有更宽的视野。本文研究了使用三维OCTA进行自动DR严重程度评估。解决此任务的一个直接方案是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。一种更轻量的解决方案是使用二维神经网络分类器处理二维正面（en-face）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采集的方式：1）正面血流图常用于检测无血管区和新生血管，2）横截面切片通常用于检测黄斑水肿等。然而，任意的数据缩减或选择可能导致信息丢失。因此，提出了两种互补策略来以二维图像最佳地摘要OCTA体积：1）通过深度学习优化的参数化正面投影，2）通过基于梯度的归因控制的横截面切片选择过程。完整的摘要和DR分类流程从端到端进行训练。自动生成的二维摘要可在查看器中显示或打印在报告中以支持决策。我们表明，所提出的二维摘要和分类流程优于直接的三维分类，并具有改进的可解释性的优势。
英文摘要：
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.
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