Optic nerve head (ONH) detection has been an important topic in the medical community for many years. Previous approaches in this domain primarily center on the analysis, localization, and detection of fundus images. However, the non-negligible discrepancy between fundus image datasets, all exclusively generated using a single type of fundus camera, challenges the generalizability of ONH detection approaches. Furthermore, despite the numerous recent semantic segmentation methods employing convolutional neural networks (CNNs) and Transformers, there is currently a lack of benchmarks for these state-of-the-art (SoTA) networks trained specifically for ONH detection. Therefore, in this article, we first introduce ODFormer, a network based on the Swin Transformer architecture. ODFormer is designed to enhance the extraction of correlation information between features, leading to improved generalizability. In our experimental evaluations, we compare our top-performing implementation with 13 SoTA CNNs and Transformers. The results indicate that our proposed ODFormer outperforms all other approaches in ONH detection. Subsequently, we release TongjiU-DCOD dataset, the first multi-resolution mixed fundus image dataset with corresponding ONH ground-truth annotations. This dataset comprises 400 fundus images captured using two different types of fundus cameras with varying resolutions. This diversity enhances the availability of data regularity information, contributing to the improved generalizability of the model. Moreover, we establish a benchmark to thoroughly evaluate the performance for ONH detection of SoTA networks designed for semantic segmentation with extensive experiments.

我们提出了一种基于Swin Transformer架构的ODFormer网络，旨在增强特征之间的相关信息提取，从而提高视神经盘检测的泛化能力。实验结果表明，我们的ODFormer在视神经盘检测方面优于其他所有方法。此外，我们发布了TongjiU-DCOD数据集，这是一个多分辨率混合视网膜图像数据集，包含用不同分辨率的两种类型的视网膜相机捕获的400个视网膜图像，并附有视神经盘的标注。我们还建立了一个基准来全面评估用于语义分割的最新网络在视神经盘检测方面的性能。

ODFormer：利用Transformer进行视网膜底片图像语义分割以实现视神经盘检测