ODFormer: Semantic Fundus Image Segmentation Using Transformer for Optic Nerve Head Detection
<|reference_start|>ODFormer: Semantic Fundus Image Segmentation Using Transformer for Optic Nerve Head Detection: 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 mias.group/ODFormer.<|reference_end|>
@article{wang2024odformer:,
    title={ODFormer: Semantic Fundus Image Segmentation Using Transformer for Optic
  Nerve Head Detection},
    author={Jiayi Wang, Yi-An Mao, Xiaoyu Ma, Sicen Guo, Yuting Shao, Xiao Lv,
  Wenting Han, Mark Christopher, Linda M. Zangwill, Yanlong Bi, Rui Fan},
    journal={arXiv preprint arXiv:2405.09552},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.09552},
    primaryClass={eess.IV cs.AI cs.CV}
}