This dataset contains 5 converted OCT datasets, part of the OmniMedSeg superset. All datasets are converted to a standardized structure with binary masks for each segmentation target.

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DATASET LICENSE AND CITATION SUMMARY

QUICK REFERENCE: DATASETS AND LICENSES

AIDK: CC0 1.0
AMD_SD: CC0 1.0
INTRARETINAL_CYSTOID_FLUID: CC-BY-NC-SA 4.0
OCT_LESION: CC-BY 4.0
OIMHS: CC0 1.0

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DETAILED INFORMATION BY DATASET

[1] AIDK

License: CC0 1.0
Dataset link: https://springernature.figshare.com/articles/dataset/An_AS-OCT_image_dataset_for_deep_learning-enabled_segmentation_and_3D_reconstruction_for_keratitis/25952845?backTo=%2Fcollections%2FAIDK_An_AS-OCT_image_dataset_for_deep_learning-enabled_segmentation_and_3D_reconstruction_for_keratitis%2F7036994&file=46760137
Metadata file: OCT/AIDK/metadata.json

Citation (bibtex):
@article{Sun2024,
author = "Yiming Sun and Nuliqiman Maimaiti and Peifang Xu and Jingxuan Cai and Pengjie Chen and Mingyu Xu and Juan Ye",
title = "{An AS-OCT image dataset for deep learning-enabled segmentation and 3D reconstruction for keratitis}",
year = "2024",
month = "6",
url = "https://springernature.figshare.com/articles/dataset/An_AS-OCT_image_dataset_for_deep_learning-enabled_segmentation_and_3D_reconstruction_for_keratitis/25952845",
doi = "10.6084/m9.figshare.25952845.v1"
}

[2] AMD_SD

License: CC0 1.0
Dataset link: https://springernature.figshare.com/articles/dataset/An_Optical_Coherence_Tomography_Image_Dataset_for_wet_AMD_Lesions_Segmentation/25513435?backTo=%2Fcollections%2FAMD-SD_An_Optical_Coherence_Tomography_Image_Dataset_for_wet_AMD_Lesions_Segmentation%2F7157554&file=48777037
Metadata file: OCT/AMD_SD/metadata.json

Citation (bibtex):
@article{Li2024,
author = "Guodong Li and Yunwei Hu and Yundi Gao and Weihao Gao and Wenbin Luo and Zhongyi Yang and Fen Xiong and Zidan Chen and Yucai Lin and Xinjing Xia and Xiaolong Yin and Yan Deng and Lan Ma",
title = "{An Optical Coherence Tomography Image Dataset for wet AMD Lesions Segmentation}",
year = "2024",
month = "9",
url = "https://springernature.figshare.com/articles/dataset/An_Optical_Coherence_Tomography_Image_Dataset_for_wet_AMD_Lesions_Segmentation/25513435",
doi = "10.6084/m9.figshare.25513435.v1"
}

[3] INTRARETINAL_CYSTOID_FLUID

License: CC-BY-NC-SA 4.0
Dataset link: https://www.kaggle.com/datasets/zeeshanahmed13/intraretinal-cystoid-fluid
Metadata file: OCT/INTRARETINAL_CYSTOID_FLUID/metadata.json

Citation (bibtex):
@article{ahmed2022deep,
title={Deep learning based automated detection of intraretinal cystoid fluid},
author={Ahmed, Zeeshan and Panhwar, Shahbaz Qamar and Baqai, Attiya and Umrani, Fahim Aziz and Ahmed, Munawar and Khan, Arbaaz},
journal={International Journal of Imaging Systems and Technology},
volume={32},
number={3},
pages={902--917},
year={2022},
publisher={Wiley Online Library}
}

[4] OCT_LESION

License: CC-BY 4.0
Source: 'license' field
Dataset link: https://www.kaggle.com/datasets/zeeshanahmed13/intraretinal-cystoid-fluid
Metadata file: OCT/OCT_LESION/metadata.json

Citation (bibtex):
@data{Yoo2020OCT,
author = {Yoo, TaeKeun},
title = {Data for: Improved accuracy in OCT diagnosis of
rare retinal disease using few-shot learning
with generative adversarial networks},
year = 2020,
version = {V2},
publisher = {Mendeley Data},
doi = {10.17632/btv6yrdbmv.2},
url = {https://doi.org/10.17632/btv6yrdbmv.2}
}

[5] OIMHS

License: CC0 1.0
Dataset link: https://springernature.figshare.com/articles/dataset/OIMHS_dataset/23508453?file=42522673
Metadata file: OCT/OIMHS/metadata.json

Citation (bibtex):
@article{Shen2023,
author = "Lijun Shen and Xin Ye and Shucheng He and Xiaxing Zhong and Yingjiao Shen and Shangchao Yang and Yiqi Chen and Xingru Huang",
title = "{OIMHS dataset}",
year = "2023",
month = "10",
url = "https://springernature.figshare.com/articles/dataset/OIMHS_dataset/23508453",
doi = "10.6084/m9.figshare.23508453.v1"
}

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IMPORTANT NOTES

• All datasets listed are publicly available
• Full metadata is stored in each dataset's metadata.json file
• For CC0-licensed datasets, attribution is appreciated but not required
• For other licenses, please review the specific terms before use