Optical Coherence Tomography Angiography (OCTA) is a promising tool for detecting Alzheimer's disease (AD) by imaging the retinal microvasculature. Ophthalmologists commonly use region-based analysis, such as the ETDRS grid, to study OCTA image biomarkers and understand the correlation with AD. However, existing studies have used general deep computer vision methods, which present challenges in providing interpretable results and leveraging clinical prior knowledge. To address these challenges, we propose a novel deep-learning framework called Polar-Net. Our approach involves mapping OCTA images from Cartesian coordinates to polar coordinates, which allows for the use of approximate sector convolution and enables the implementation of the ETDRS grid-based regional analysis method commonly used in clinical practice. Furthermore, Polar-Net incorporates clinical prior information of each sector region into the training process, which further enhances its performance. Additionally, our framework adapts to acquire the importance of the corresponding retinal region, which helps researchers and clinicians understand the model's decision-making process in detecting AD and assess its conformity to clinical observations. Through evaluations on private and public datasets, we have demonstrated that Polar-Net outperforms existing state-of-the-art methods and provides more valuable pathological evidence for the association between retinal vascular changes and AD. In addition, we also show that the two innovative modules introduced in our framework have a significant impact on improving overall performance.

翻译：光学相干断层扫描血管成像（OCTA）通过视网膜微血管成像，为检测阿尔茨海默病（AD）提供了具有前景的工具。眼科医生通常使用基于区域的分析方法（如ETDRS网格）研究OCTA图像生物标志物，并理解其与AD的相关性。然而，现有研究采用通用深度计算机视觉方法，在提供可解释性结果和利用临床先验知识方面存在挑战。为解决这些问题，我们提出了一种名为Polar-Net的新型深度学习框架。该方法将OCTA图像从笛卡尔坐标映射至极坐标，从而可使用近似扇形卷积，并实现临床实践中常用的ETDRS网格区域分析。此外，Polar-Net将每个扇形区域的临床先验信息融入训练过程，进一步提升了性能。该框架还能自适应获取对应视网膜区域的重要性，帮助研究人员和临床医生理解模型在AD检测中的决策过程，并评估其是否符合临床观察。通过在私有和公共数据集上的评估，我们证明Polar-Net优于现有最先进方法，并为视网膜血管变化与AD之间的关联提供了更有价值的病理学证据。同时，我们展示了框架中引入的两个创新模块对提升整体性能具有显著作用。