优化U-Net结构与损失函数，提升RGB眼底图中老年黄斑变性病灶分割精度
Surpassing state of the art on AMD area estimation from RGB fundus images through careful selection of U-Net architectures and loss functions for class imbalance
- 基于U-Net框架，系统筛选不同编码器与损失函数组合
- 在ADAM挑战赛数据集上实现多类病变分割性能超越已有最佳结果
- 适合医学图像分割、眼科疾病智能诊断研究者参考
老年黄斑变性（AMD）是60岁以上人群导致不可逆视力损伤的主要原因。本研究聚焦于在RGB眼底图像中进行AMD病灶的语义分割，该方法为非侵入性且成本低廉的成像技术。研究以目前最全面的AMD检测竞赛——ADAM挑战赛及其公开数据集为基准评估标准。基于U-Net连接结构，系统评估并比较了多种改进策略：预处理方法、不同复杂度的编码器（骨干网络）、以及用于缓解图像级和像素级类别不平衡的专用损失函数。最终构建的AMD检测框架，在非侵入性RGB眼底图像中对多种类型AMD病灶的多类别分割任务上，超越了所有先前的ADAM挑战赛提交结果。本文实验所用源代码已公开。
原文摘要 · Abstract (English)
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.
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