This dataset contains 24 converted MRI 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

Please read the Description for more details about the dataset licenses and terms of use.

ACDC: CC BY-NC-SA 4.0
CC_TUMOR_HETEROGENEITY: CC-BY 4.0
CDEMRIS: CC-BY 4.0
CHAOS_MRI: CC BY-NC-SA 4.0
CROSSMODA: CC BY-NC-SA 4.0
GBM_RESERVOIR: CC-BY 4.0
HNTSMRG: CC-BY-NC 4.0
HVSMR_20: CC-BY 4.0
HYPO_SUBFIELDS: CC-BY 4.0
IBD: CC-BY 4.0
ISLES_2022: Creative Commons Attribution 4.0 International
MEDSEG_VENTRICLES: CC0 1.0
MSD_HEART: CC BY-SA 4.0
MSD_PROSTATE: CC BY-SA 4.0
NCI_ISBI: CC-BY 3.0
PICAI: CC-BY-NC 4.0
PITUITORY_TUMOR: CC-BY 4.0
PROMISE12: "You can use this data for your research. If you do that it is mandatory to cite https://doi.org/10.1016/j.media.2013.12.002."
QIN_PROSTATE: CC-BY 4.0
RESECT: CC BY-NC-SA 4.0
SEGTHY: CC-BY 4.0
SPIDER: CC-BY 4.0
ATRIASEG_2018: 'You may download the training and testing data'
SPINE: 'Together with this submission, we make our program code publicly available under an open-source license, including 17 anonymized datasets with corresponding segmentations.'
TOM500: CC0 1.0

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DETAILED INFORMATION BY DATASET

[1] ACDC

License: CC BY-NC-SA 4.0
Dataset link: https://humanheart-project.creatis.insa-lyon.fr/database/#collection/637218c173e9f0047faa00fb/folder/637218e573e9f0047faa00fc
Metadata file: MRI/ACDC/metadata.json

Citation (bibtex):
@article{Bernard2018,
title = {Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?},
volume = {37},
ISSN = {1558-254X},
url = {http://dx.doi.org/10.1109/TMI.2018.2837502},
DOI = {10.1109/tmi.2018.2837502},
number = {11},
journal = {IEEE Transactions on Medical Imaging},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Bernard, Olivier and Lalande, Alain and Zotti, Clement and Cervenansky, Frederick and Yang, Xin and Heng, Pheng-Ann and Cetin, Irem and Lekadir, Karim and Camara, Oscar and Gonzalez Ballester, Miguel Angel and Sanroma, Gerard and Napel, Sandy and Petersen, Steffen and Tziritas, Georgios and Grinias, Elias and Khened, Mahendra and Kollerathu, Varghese Alex and Krishnamurthi, Ganapathy and Rohé, Marc-Michel and Pennec, Xavier and Sermesant, Maxime and Isensee, Fabian and J"{a}ger, Paul and Maier-Hein, Klaus H. and Full, Peter M. and Wolf, Ivo and Engelhardt, Sandy and Baumgartner, Christian F. and Koch, Lisa M. and Wolterink, Jelmer M. and Išgum, Ivana and Jang, Yeonggul and Hong, Yoonmi and Patravali, Jay and Jain, Shubham and Humbert, Olivier and Jodoin, Pierre-Marc},
year = {2018},
month = Nov,
pages = {2514–2525}
}

[2] ATRIASEG_2018

License: 'You may download the training and testing data'

Source: 'comments' field (no license or custom_license found)
Dataset link: https://www.cardiacatlas.org/atriaseg2018-challenge/atria-seg-data/
Metadata file: MRI/ATRIASEG_2018/metadata.json

Citation (bibtex):
@article{Xiong2021,
title = {A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging},
volume = {67},
ISSN = {1361-8415},
url = {http://dx.doi.org/10.1016/j.media.2020.101832},
DOI = {10.1016/j.media.2020.101832},
journal = {Medical Image Analysis},
publisher = {Elsevier BV},
author = {Xiong, Zhaohan and Xia, Qing and Hu, Zhiqiang and Huang, Ning and Bian, Cheng and Zheng, Yefeng and Vesal, Sulaiman and Ravikumar, Nishant and Maier, Andreas and Yang, Xin and Heng, Pheng-Ann and Ni, Dong and Li, Caizi and Tong, Qianqian and Si, Weixin and Puybareau, Elodie and Khoudli, Younes and Géraud, Thierry and Chen, Chen and Bai, Wenjia and Rueckert, Daniel and Xu, Lingchao and Zhuang, Xiahai and Luo, Xinzhe and Jia, Shuman and Sermesant, Maxime and Liu, Yashu and Wang, Kuanquan and Borra, Davide and Masci, Alessandro and Corsi, Cristiana and de Vente, Coen and Veta, Mitko and Karim, Rashed and Preetha, Chandrakanth Jayachandran and Engelhardt, Sandy and Qiao, Menyun and Wang, Yuanyuan and Tao, Qian and Nuñez-Garcia, Marta and Camara, Oscar and Savioli, Nicolo and Lamata, Pablo and Zhao, Jichao},
year = {2021},
month = Jan,
pages = {101832}
}

[3] CC_TUMOR_HETEROGENEITY

License: CC-BY 4.0
Dataset link: https://www.cancerimagingarchive.net/collection/cc-tumor-heterogeneity/
Metadata file: MRI/CC_TUMOR_HETEROGENEITY/metadata.json

Citation (bibtex):
@misc{https://doi.org/10.7937/erz5-qz59,
doi = {10.7937/ERZ5-QZ59},
url = {https://www.cancerimagingarchive.net/collection/cc-tumor-heterogeneity/},
author = {Mayr, Nina and Yuh, William T.C. and Bowen, Stephen and Harkenrider, Matthew and Knopp, Michael V. and Lee, Elaine Yuen-Phin and Leung, Eric and Lo, Simon S. and Small Jr., William and Wolfson, Aaron H.},
title = {Cervical Cancer - Tumor Heterogeneity: Serial Functional and Molecular Imaging Across the Radiation Therapy Course in Advanced Cervical Cancer (CC-Tumor-Heterogeneity)},
publisher = {The Cancer Imaging Archive},
year = {2023},
copyright = {Creative Commons Attribution 4.0 International}
}

[4] CDEMRIS

License: CC-BY 4.0
Dataset link: https://figshare.com/articles/dataset/CDEMRIS_fibrosis_scar_challenge_data_2012/4214532
Metadata file: MRI/CDEMRIS/metadata.json

Citation (bibtex):
@article{Karim2016,
author = "Rashed Karim",
title = "{CDEMRIS fibrosis scar challenge dataset}",
year = "2016",
month = "11",
url = "https://figshare.com/articles/dataset/CDEMRIS_fibrosis_scar_challenge_data_2012/4214532",
doi = "10.6084/m9.figshare.4214532.v5"
}

[5] CHAOS_MRI

License: CC BY-NC-SA 4.0
Dataset link: https://zenodo.org/records/3431873
Metadata file: MRI/CHAOS_MRI/metadata.json

Citation (bibtex):
@article{kavur2019,
title = {Comparison of semi-automatic and deep learning based automatic methods for liver segmentation in living liver transplant donors},
author = {Kavur, A. Emre and Gezer, Naciye Sinem and Barış, Mustafa and Şahin, Yusuf and Özkan, Savaş and Baydar,Bora and Yüksel, Ulaş and Kılıkçıer, Çağlar and Olut, Şahin and Bozdağı Akar, Gözde and Ünal, Gözde and Dicle, Oğuz and Selver, M. Alper},
journal = {Diagnostic and Interventional Radiology},
volume = {26},
pages = {11-21},
year = {2020},
month = Jan,
doi = {10.5152/dir.2019.19},
url = {https://doi.org/10.5152/dir.2019.19025}
}

[6] CROSSMODA

License: CC BY-NC-SA 4.0
Dataset link: https://www.cancerimagingarchive.net/collection/vestibular-schwannoma-seg/
Metadata file: MRI/CROSSMODA/metadata.json

Citation (bibtex):
@article{Shapey2021,
title = {Segmentation of vestibular schwannoma from MRI — An open annotated dataset and baseline algorithm},
url = {http://dx.doi.org/10.1101/2021.08.04.21261588},
DOI = {10.1101/2021.08.04.21261588},
publisher = {openRxiv},
author = {Shapey, Jonathan and Kujawa, Aaron and Dorent, Reuben and Wang, Guotai and Dimitriadis, Alexis and Grishchuk, Diana and Paddick, Ian and Kitchen, Neil and Bradford, Robert and Saeed, Shakeel R and Bisdas, Sotirios and Ourselin, Sébastien and Vercauteren, Tom},
year = {2021},
month = Aug
}

[7] GBM_RESERVOIR

License: CC-BY 4.0
Dataset link: https://figshare.com/articles/dataset/GBM-Reservoir_Dataset_and_Segmentations/28001450
Metadata file: MRI/GBM_RESERVOIR/metadata.json

Citation (bibtex):
@article{Solak2024,
author = "Naida Solak and André Ferreira and Gijs Luijten and Behrus Puladi and Victor Alves and Jan Egger",
title = "{GBM-Reservoir: Dataset and Segmentations}",
year = "2024",
month = "12",
url = "https://figshare.com/articles/dataset/GBM-Reservoir_Dataset_and_Segmentations/28001450",
doi = "10.6084/m9.figshare.28001450.v20"
}

[8] HNTSMRG

License: CC-BY-NC 4.0
Dataset link: https://zenodo.org/records/11199559
Metadata file: MRI/HNTSMRG/metadata.json

Citation (bibtex):
@misc{https://doi.org/10.5281/zenodo.11199559,
doi = {10.5281/ZENODO.11199559},
url = {https://zenodo.org/doi/10.5281/zenodo.11199559},
author = {Wahid, Kareem and Dede, Cem and Naser, Mohamed and Fuller, Clifton},
keywords = {Magnetic resonance imaging, Artificial intelligence, Machine learning, segmentation, radiotherapy, Head and neck cancer, oropharyngeal cancer, radiation oncology},
language = {en},
title = {Training Dataset for HNTSMRG 2024 Challenge},
publisher = {Zenodo},
year = {2024},
copyright = {Creative Commons Attribution Non Commercial 4.0 International}
}

[9] HVSMR_20

License: CC-BY 4.0
Dataset link: https://figshare.com/articles/dataset/HVSMR-2_0_orig_/25226360?backTo=/collections/HVSMR-2_0_A_3D_cardiovascular_MR_dataset_for_whole-heart_segmentation_in_congenital_heart_disease/7074755
Metadata file: MRI/HVSMR_20/metadata.json

Citation (bibtex):
@article{Pace2024,
author = "Danielle Pace and Hannah Contreras and Jennifer Romanowicz and Shruti Ghelani and Imon Rahaman and Yue Zhang and Patricia Gao and mohammad imrul jubair and Tom Yeh and Polina Golland and Tal Geva and Sunil Ghelani and Andrew Powell and mehdi hedjazi moghari",
title = "{HVSMR-2.0 (orig)}",
year = "2024",
month = "2",
url = "https://figshare.com/articles/dataset/HVSMR-2_0_orig_/25226360",
doi = "10.6084/m9.figshare.25226360.v2"
}

[10] HYPO_SUBFIELDS

License: CC-BY 4.0
Dataset link: https://plus.figshare.com/articles/dataset/A_paired_dataset_of_multi-modal_MRI_at_3_Tesla_and_7_Tesla_with_manual_hippocampal_subfield_segmentations_on_7T_T2-weighted_images/26075713/1
Metadata file: MRI/HYPO_SUBFIELDS/metadata.json

Citation (bibtex):
@article{Li2024,
author = "Shuyu Li and Lei Chu and Baoqiang Ma and Xiaoxi Dong and Yirong He and Debin Zeng and Tongtong Che",
title = "{A paired dataset of multi-modal MRI at 3 Tesla and 7 Tesla with manual hippocampal subfield segmentations on 7T T2-weighted images}",
year = "2024",
month = "10",
url = "https://plus.figshare.com/articles/dataset/A_paired_dataset_of_multi-modal_MRI_at_3_Tesla_and_7_Tesla_with_manual_hippocampal_subfield_segmentations_on_7T_T2-weighted_images/26075713",
doi = "10.25452/figshare.plus.26075713.v1"
}

[11] IBD

License: CC-BY 4.0
Dataset link: https://zenodo.org/records/13839321
Metadata file: MRI/IBD/metadata.json

Citation (bibtex):
@article{Zhong2025,
title = {A comprehensive dataset of magnetic resonance enterography images with intestinal segment annotations},
volume = {12},
ISSN = {2052-4463},
url = {http://dx.doi.org/10.1038/s41597-025-04760-z},
DOI = {10.1038/s41597-025-04760-z},
number = {1},
journal = {Scientific Data},
publisher = {Springer Science and Business Media LLC},
author = {Zhong, Zhangnan and Huang, Li and Feng, Shi-Ting and Lin, Haiwei and Wang, Xinyue and Lu, Baolan and Cao, Kangyang and Li, Xuehua and Huang, Bingsheng},
year = {2025},
month = Mar
}

[12] ISLES_2022

License: Creative Commons Attribution 4.0 International
Dataset link: https://zenodo.org/records/7960856
Metadata file: MRI/ISLES_2022/metadata.json

Citation (bibtex):
@MISC{Hernandez_Petzsche2022-iv,
title = "{ISLES} 2022: A multi-center magnetic resonance imaging stroke
lesion segmentation dataset",
author = "Hernandez Petzsche, Moritz Roman and de la Rosa, Ezequiel and
Wiest, Roland and Hanning, Uta and Wiestler, Benedikt and
Kirschke, Jan S",
publisher = "Zenodo",
year = 2022
}

[13] MEDSEG_VENTRICLES

License: CC0 1.0
Dataset link: https://figshare.com/articles/dataset/MedSeg_Ventricles_MRI_Dataset/19644636/1
Metadata file: MRI/MEDSEG_VENTRICLES/metadata.json

Citation (bibtex):
@article{MedSeg2022,
author = "MedSeg and Tomas Sakinis and Håvard Bjørke Jenssen",
title = "{MedSeg Ventricles MRI Dataset}",
year = "2022",
month = "4",
url = "https://figshare.com/articles/dataset/MedSeg_Ventricles_MRI_Dataset/19644636",
doi = "10.6084/m9.figshare.19644636.v1"
}

[14] MSD_HEART

License: CC BY-SA 4.0
Dataset link: http://medicaldecathlon.com/#tasks
Metadata file: MRI/MSD_HEART/metadata.json

Citation (bibtex):
@misc{https://doi.org/10.48550/arxiv.1902.09063,
doi = {10.48550/ARXIV.1902.09063},
url = {https://arxiv.org/abs/1902.09063},
author = {Simpson, Amber L. and Antonelli, Michela and Bakas, Spyridon and Bilello, Michel and Farahani, Keyvan and van Ginneken, Bram and Kopp-Schneider, Annette and Landman, Bennett A. and Litjens, Geert and Menze, Bjoern and Ronneberger, Olaf and Summers, Ronald M. and Bilic, Patrick and Christ, Patrick F. and Do, Richard K. G. and Gollub, Marc and Golia-Pernicka, Jennifer and Heckers, Stephan H. and Jarnagin, William R. and McHugo, Maureen K. and Napel, Sandy and Vorontsov, Eugene and Maier-Hein, Lena and Cardoso, M. Jorge},
keywords = {Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},
title = {A large annotated medical image dataset for the development and evaluation of segmentation algorithms},
publisher = {arXiv},
year = {2019},
copyright = {arXiv.org perpetual, non-exclusive license}
}

[15] MSD_PROSTATE

License: CC BY-SA 4.0
Dataset link: http://medicaldecathlon.com/#tasks
Metadata file: MRI/MSD_PROSTATE/metadata.json

Citation (bibtex):
@misc{https://doi.org/10.48550/arxiv.1902.09063,
doi = {10.48550/ARXIV.1902.09063},
url = {https://arxiv.org/abs/1902.09063},
author = {Simpson, Amber L. and Antonelli, Michela and Bakas, Spyridon and Bilello, Michel and Farahani, Keyvan and van Ginneken, Bram and Kopp-Schneider, Annette and Landman, Bennett A. and Litjens, Geert and Menze, Bjoern and Ronneberger, Olaf and Summers, Ronald M. and Bilic, Patrick and Christ, Patrick F. and Do, Richard K. G. and Gollub, Marc and Golia-Pernicka, Jennifer and Heckers, Stephan H. and Jarnagin, William R. and McHugo, Maureen K. and Napel, Sandy and Vorontsov, Eugene and Maier-Hein, Lena and Cardoso, M. Jorge},
keywords = {Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},
title = {A large annotated medical image dataset for the development and evaluation of segmentation algorithms},
publisher = {arXiv},
year = {2019},
copyright = {arXiv.org perpetual, non-exclusive license}
}

[16] NCI_ISBI

License: CC-BY 3.0
Dataset link: https://www.cancerimagingarchive.net/analysis-result/isbi-mr-prostate-2013/
Metadata file: MRI/NCI_ISBI/metadata.json

Citation (bibtex):
@misc{https://doi.org/10.7937/k9/tcia.2015.zf0vlopv,
doi = {10.7937/K9/TCIA.2015.ZF0VLOPV},
url = {https://www.cancerimagingarchive.net/analysis-result/isbi-mr-prostate-2013/},
author = {Bloch, B. Nicholas and Madabhushi, Anant and Huisman, Henkjan and Freymann, John and Kirby, Justin and Grauer, Michael and Enquobahrie, Andinet and Jaffe, Carl and Clarke, Larry and Farahani, Keyvan},
title = {NCI-ISBI 2013 Challenge: Automated Segmentation of Prostate Structures (ISBI-MR-Prostate-2013)},
publisher = {The Cancer Imaging Archive},
year = {2015},
copyright = {Creative Commons Attribution 3.0 Unported}
}

[17] PICAI

License: CC-BY-NC 4.0
Dataset link: https://zenodo.org/records/6624726
Metadata file: MRI/PICAI/metadata.json

Citation (bibtex):
@misc{https://doi.org/10.5281/zenodo.6624726,
doi = {10.5281/ZENODO.6624726},
url = {https://zenodo.org/record/6624726},
author = {Saha, Anindo and Twilt, Jasper Jonathan and Bosma, Joeran Sander and van Ginneken, Bram and Yakar, Derya and Elschot, Mattijs and Veltman, Jeroen and F"{u}tterer, Jurgen and de Rooij, Maarten and Huisman, Henkjan},
keywords = {prostate cancer, artificial intelligence, magnetic resonance imaging, radiologists, computer-aided detection and diagnosis},
language = {en},
title = {The PI-CAI Challenge: Public Training and Development Dataset},
publisher = {Zenodo},
year = {2022},
copyright = {Creative Commons Attribution Non Commercial 4.0 International}
}

[18] PITUITORY_TUMOR

License: CC-BY 4.0
Metadata file: MRI/PITUITORY_TUMOR/metadata.json

Citation (bibtex):
@article{Pandit2025,
author = "Anand Pandit and Andrew Keenlyside and Danyal Khan and Gerda Reischer and Muhammad Kamal and Nina Yoh and Zane Jaunmuktane and Anouk Borg and Neil Dorward and Stefanie Baldeweg and Indran Davagnanam and Harpreet Hyare and Parashkev Nachev and Hani Marcus",
title = "{Mapping pituitary neuroendocrine tumors: an annotated MRI dataset profiling tumor and carotid characteristics}",
year = "2025",
month = "1",
url = "https://springernature.figshare.com/articles/dataset/Mapping_pituitary_neuroendocrine_tumors_an_annotated_MRI_dataset_profiling_tumor_and_carotid_characteristics/27894084",
doi = "10.6084/m9.figshare.27894084.v1"
}

[19] PROMISE12

License: "You can use this data for your research. If you do that it is mandatory to cite https://doi.org/10.1016/j.media.2013.12.002."
Dataset link: https://zenodo.org/records/8026660
Metadata file: MRI/PROMISE12/metadata.json

Citation (bibtex):
@misc{https://doi.org/10.5281/zenodo.8026660,
doi = {10.5281/ZENODO.8026660},
url = {https://zenodo.org/record/8026660},
author = {Litjens, Geert and Van Ginneken, Bram and {Henkjan Huisman} and Van De Ven, Wendy and Hoeks, Caroline and Barratt, Dean and {Anant Madabhushi}},
keywords = {MRI, prostate, segmentation, medical image analysis},
language = {en},
title = {PROMISE12: Data from the MICCAI Grand Challenge: Prostate MR Image Segmentation 2012},
publisher = {Zenodo},
year = {2023},
copyright = {Open Access}
}

[20] QIN_PROSTATE

License: CC-BY 4.0
Dataset link: https://www.cancerimagingarchive.net/collection/qin-prostate-repeatability/
Metadata file: MRI/QIN_PROSTATE/metadata.json

Citation (bibtex):
@article{Fedorov2017,
title = {Multiparametric Magnetic Resonance Imaging of the Prostate: Repeatability of Volume and Apparent Diffusion Coefficient Quantification},
volume = {52},
ISSN = {0020-9996},
url = {http://dx.doi.org/10.1097/RLI.0000000000000382},
DOI = {10.1097/rli.0000000000000382},
number = {9},
journal = {Investigative Radiology},
publisher = {Ovid Technologies (Wolters Kluwer Health)},
author = {Fedorov, Andriy and Vangel, Mark G. and Tempany, Clare M. and Fennessy, Fiona M.},
year = {2017},
month = Sept,
pages = {538–546}
}

[21] RESECT

License: CC BY-NC-SA 4.0
Dataset link: https://osf.io/jv8bk/
Metadata file: MRI/RESECT/metadata.json

Citation (bibtex):
@article{Xiao2017,
title = {REtroSpective Evaluation of Cerebral Tumors (RESECT): A clinical database of pre-operative MRI and intra-operative ultrasound in low-grade glioma surgeries},
volume = {44},
ISSN = {0094-2405},
url = {http://dx.doi.org/10.1002/mp.12268},
DOI = {10.1002/mp.12268},
number = {7},
journal = {Medical Physics},
publisher = {Wiley},
author = {Xiao, Yiming and Fortin, Maryse and Unsgård, Geirmund and Rivaz, Hassan and Reinertsen, Ingerid},
year = {2017},
month = May,
pages = {3875–3882}
}

[22] SEGTHY

License: CC-BY 4.0
Dataset link: https://www.cs.cit.tum.de/camp/publications/segthy-dataset/
Metadata file: MRI/SEGTHY/metadata.json

Citation (bibtex):
@article{Krnke2022,
title = {Tracked 3D ultrasound and deep neural network-based thyroid segmentation reduce interobserver variability in thyroid volumetry},
volume = {17},
ISSN = {1932-6203},
url = {http://dx.doi.org/10.1371/journal.pone.0268550},
DOI = {10.1371/journal.pone.0268550},
number = {7},
journal = {PLOS ONE},
publisher = {Public Library of Science (PLoS)},
author = {Kr"{o}nke, Markus and Eilers, Christine and Dimova, Desislava and K"{o}hler, Melanie and Buschner, Gabriel and Schweiger, Lilit and Konstantinidou, Lemonia and Makowski, Marcus and Nagarajah, James and Navab, Nassir and Weber, Wolfgang and Wendler, Thomas},
editor = {V E, Sathishkumar},
year = {2022},
month = July,
pages = {e0268550}
}

[23] SPIDER

License: CC-BY 4.0
Dataset link: https://zenodo.org/records/10159290
Metadata file: MRI/SPIDER/metadata.json

Citation (bibtex):
@article{vanderGraaf2024,
title = {Lumbar spine segmentation in MR images: a dataset and a public benchmark},
volume = {11},
ISSN = {2052-4463},
url = {http://dx.doi.org/10.1038/s41597-024-03090-w},
DOI = {10.1038/s41597-024-03090-w},
number = {1},
journal = {Scientific Data},
publisher = {Springer Science and Business Media LLC},
author = {van der Graaf, Jasper W. and van Hooff, Miranda L. and Buckens, Constantinus F. M. and Rutten, Matthieu and van Susante, Job L. C. and Kroeze, Robert Jan and de Kleuver, Marinus and van Ginneken, Bram and Lessmann, Nikolas},
year = {2024},
month = Mar
}

[24] SPINE

License: 'Together with this submission, we make our program code publicly available under an open-source license, including 17 anonymized datasets with corresponding segmentations.'
Dataset link: https://www.cg.informatik.uni-siegen.de/en/spine-segmentation-and-analysis
Metadata file: MRI/SPINE/metadata.json

Citation (bibtex):
@article{Zuki2014,
title = {Robust Detection and Segmentation for Diagnosis of Vertebral Diseases Using Routine MR Images},
volume = {33},
ISSN = {1467-8659},
url = {http://dx.doi.org/10.1111/cgf.12343},
DOI = {10.1111/cgf.12343},
number = {6},
journal = {Computer Graphics Forum},
publisher = {Wiley},
author = {Zukić, Dženan and Vlasák, Aleš and Egger, Jan and Hořínek, Daniel and Nimsky, Christopher and Kolb, Andreas},
year = {2014},
month = Mar,
pages = {190–204}
}

[25] TOM500

License: CC0 1.0
Dataset link: https://springernature.figshare.com/articles/dataset/TOM500_A_Multi-Organ_Annotated_Orbital_MRI_Dataset_for_Thyroid_Eye_Disease/27133389?file=49499655
Metadata file: MRI/TOM500/metadata.json

Citation (bibtex):
@article{Song2025,
author = "Xuefei Song and Huifang Zhou and Xianqun Fan and Haiyang Zhang and Hoi Chi Chan and Jiashuo Xu and Mengda Jiang and Xiaofeng Tao",
title = "{TOM500: A Multi-Organ Annotated Orbital MRI Dataset for Thyroid Eye Disease}",
year = "2025",
month = "1",
url = "https://springernature.figshare.com/articles/dataset/TOM500_A_Multi-Organ_Annotated_Orbital_MRI_Dataset_for_Thyroid_Eye_Disease/27133389",
doi = "10.6084/m9.figshare.27133389.v1"
}

================================================================================
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