Age-related macular degeneration (AMD) is one of the leading causes of irreversible vision impairment in people over the age of 60. This research focuses on semantic segmentation for AMD lesion detection in RGB fundus images, a non-invasive and cost-effective imaging technique. The results of the ADAM challenge - the most comprehensive AMD detection from RGB fundus images research competition and open dataset to date - serve as a benchmark for our evaluation. Taking the U-Net connectivity as a base of our framework, we evaluate and compare several approaches to improve the segmentation model's architecture and training pipeline, including pre-processing techniques, encoder (backbone) deep network types of varying complexity, and specialized loss functions to mitigate class imbalances on image and pixel levels. The main outcome of this research is the final configuration of the AMD detection framework, which outperforms all the prior ADAM challenge submissions on the multi-class segmentation of different AMD lesion types in non-invasive RGB fundus images. The source code used to conduct the experiments presented in this paper is made freely available.

翻译：年龄相关性黄斑变性（AMD）是60岁以上人群不可逆视力损伤的主要原因之一。本研究聚焦于RGB眼底图像中AMD病灶检测的语义分割任务，这是一种非侵入性且成本效益高的成像技术。我们以ADAM挑战赛（迄今最全面的RGB眼底图像AMD检测研究竞赛及开放数据集）的结果作为评估基准。以U-Net连接结构为框架基础，我们系统评估并比较了多种改进分割模型架构与训练流程的方法，包括预处理技术、不同复杂度的编码器（骨干）深度网络类型，以及专门针对图像级和像素级类别不平衡设计的损失函数。本研究的主要成果是最终构建的AMD检测框架配置，其在非侵入性RGB眼底图像中对不同AMD病灶类型的多类别分割任务上，超越了所有先前ADAM挑战赛的提交方案。本文实验所用源代码已公开提供。