Optic nerve head (ONH) detection has been a crucial area of study in ophthalmology for years. However, the significant discrepancy between fundus image datasets, each generated using a single type of fundus camera, poses challenges to the generalizability of ONH detection approaches developed based on semantic segmentation networks. Despite the numerous recent advancements in general-purpose semantic segmentation methods using convolutional neural networks (CNNs) and Transformers, there is currently a lack of benchmarks for these state-of-the-art (SoTA) networks specifically trained for ONH detection. Therefore, in this article, we make contributions from three key aspects: network design, the publication of a dataset, and the establishment of a comprehensive benchmark. Our newly developed ONH detection network, referred to as ODFormer, is based upon the Swin Transformer architecture and incorporates two novel components: a multi-scale context aggregator and a lightweight bidirectional feature recalibrator. Our published large-scale dataset, known as TongjiU-DROD, provides multi-resolution fundus images for each participant, captured using two distinct types of cameras. Our established benchmark involves three datasets: DRIONS-DB, DRISHTI-GS1, and TongjiU-DROD, created by researchers from different countries and containing fundus images captured from participants of diverse races and ages. Extensive experimental results demonstrate that our proposed ODFormer outperforms other state-of-the-art (SoTA) networks in terms of performance and generalizability. Our dataset and source code are publicly available at .

中文翻译：

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ODFormer：使用 Transformer 进行视神经乳头检测的语义眼底图像分割


多年来，视神经乳头 (ONH) 检测一直是眼科研究的一个重要领域。然而，使用单一类型眼底相机生成的眼底图像数据集之间存在显着差异，这对基于语义分割网络开发的 ONH 检测方法的通用性提出了挑战。尽管使用卷积神经网络 (CNN) 和 Transformer 的通用语义分割方法最近取得了许多进展，但目前缺乏这些专门针对 ONH 检测训练的最先进 (SoTA) 网络的基准。因此，在本文中，我们从三个关键方面做出了贡献：网络设计、数据集的发布以及综合基准的建立。我们新开发的 ONH 检测网络（称为 ODFormer）基于 Swin Transformer 架构，并包含两个新颖的组件：多尺度上下文聚合器和轻量级双向特征重新校准器。我们发布的大型数据集（称为 TongjiU-DROD）为每个参与者提供了使用两种不同类型的相机捕获的多分辨率眼底图像。我们建立的基准涉及三个数据集：DRIONS-DB、DRISHTI-GS1 和 TongjiU-DROD，由来自不同国家的研究人员创建，包含从不同种族和年龄的参与者捕获的眼底图像。大量的实验结果表明，我们提出的 ODFormer 在性能和通用性方面优于其他最先进的 (SoTA) 网络。我们的数据集和源代码可在 公开获取。