Different from the previous combined multi-domain dataset for unsupervised domain adaptation (UDA) in medical image segmentation, this multi-domain fundus image dataset contains annotations made by the same group of ophthalmologists. Hence the annotator bias among different datasets can be mitigated. Therefore, this dataset can provide a relatively fair benchmark for evaluating UDA methods in fundus image segmentation. This dataset is based on the RIGA[1] dataset and MESSIDOR[2] dataset. We appreciate their efforts devoted by the authors of [1] and [2]. The six duplicated cases in the RIGA dataset are filtered out according to the Errata. We also remove the duplicated cases that exist in both the RIGA dataset and the MESSIDOR dataset by hash value matching. Details of the RIGA+ dataset Domain Dataset Labeled Samples (Train+Test) Unlabeled Samples Source BinRushed 195 (195+0) 0 Source Magrabia 95 (95+0) 0 Target MESSIDOR-BASE1 173 (138+35) 227 Target MESSIDOR-BASE2 148 (118+30) 238 Target MESSIDOR-BASE3 133 (106+27) 252 [1] Almazroa A, Alodhayb S, Osman E, et al. Retinal fundus images for glaucoma analysis: the RIGA dataset[C]//Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications. International Society for Optics and Photonics, 2018, 10579: 105790B. [2] Decencière E, Zhang X, Cazuguel G, et al. Feedback on a publicly distributed image database: the Messidor database[J]. Image Analysis & Stereology, 2014, 33(3): 231-234. If you find this dataset useful for your research, please consider citing the paper as follows: @inproceedings{hu2022domain, title={Domain Specific Convolution and High Frequency Reconstruction based Unsupervised Domain Adaptation for Medical Image Segmentation}, author={Shishuai Hu and Zehui Liao and Yong Xia}, booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention}, year={2022}, organization={Springer} }