2026-10-12T02:46:59Zhttps://www.radar-service.eu/oai/OAIHandler10.35097/rz38r5geakmb4t8m2026-10-09T11:06:44Zradar4kit
https://dx.doi.org/10.35097/rz38r5geakmb4t8m
Marinov, Zdravko
OmniMedSeg-Microscopy
Computer Vision for Human Computer Interactions Lab (cv:hci), Institute for Anthropomatics and Robotics (IAR), Karlsruhe Institute of Technology
2026
Computer Science
dataset
Dataset
info:eu-repo/semantics/openAccess
Other
info:eu-repo/semantics/openAccess
Please read the Description for more details about the dataset licenses and terms of use. Licenses: BBBC010: CC0 1.0 BBBC038: CC0 1.0 BBBC041SEG: MIT License BRIFISEG: CC-BY 4.0 CCAGT: CC-BY-NC 3.0 EMDS_6: CC-BY 4.0 FLUORESCENT_NEURONAL_CELLS: CC-BY 4.0 OCCISC: "the database used in this challenge is currently publicly available" PCMMD: CC-BY 4.0 SLIMIA: CC-BY 4.0 SSTEM: CC BY-NC-SA 3.0 VICAR: CC-BY 4.0 WBC: GPL-3.0 YEAZ: CC-BY 4.0
This dataset contains 14 converted Microscopy 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
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QUICK REFERENCE: DATASETS AND LICENSES
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BBBC010: CC0 1.0
BBBC038: CC0 1.0
BBBC041SEG: MIT License
BRIFISEG: CC-BY 4.0
CCAGT: CC-BY-NC 3.0
EMDS_6: CC-BY 4.0
FLUORESCENT_NEURONAL_CELLS: CC-BY 4.0
OCCISC: "the database used in this challenge is currently publicly available"
PCMMD: CC-BY 4.0
SLIMIA: CC-BY 4.0
SSTEM: CC BY-NC-SA 3.0
VICAR: CC-BY 4.0
WBC: GPL-3.0
YEAZ: CC-BY 4.0
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DETAILED INFORMATION BY DATASET
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[1] BBBC010
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License: CC0 1.0
Dataset link: https://bbbc.broadinstitute.org/BBBC010
Metadata file: Microscopy/BBBC010/metadata.json
Citation (bibtex):
@article{moy2009high,
title={High-throughput screen for novel antimicrobials using a whole animal infection model},
author={Moy, Terence I and Conery, Annie L and Larkins-Ford, Jonah and Wu, Gang and Mazitschek, Ralph and Casadei, Gabriele and Lewis, Kim and Carpenter, Anne E and Ausubel, Frederick M},
journal={ACS chemical biology},
volume={4},
number={7},
pages={527--533},
year={2009},
publisher={ACS Publications}
}
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[2] BBBC038
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License: CC0 1.0
Dataset link: https://bbbc.broadinstitute.org/BBBC038
Metadata file: Microscopy/BBBC038/metadata.json
Citation (bibtex):
@article{caicedo2019nucleus,
title={Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl},
author={Caicedo, Juan C and Goodman, Allen and Karhohs, Kyle W and Cimini, Beth A and Ackerman, Jeanelle and Haghighi, Marzieh and Heng, CherKeng and Becker, Tim and Doan, Minh and McQuin, Claire and others},
journal={Nature methods},
volume={16},
number={12},
pages={1247--1253},
year={2019},
publisher={Nature Publishing Group US New York}
}
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[3] BBBC041SEG
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License: MIT License
Dataset link: https://github.com/Deponker/Blood-cell-segmentation
Metadata file: Microscopy/BBBC041SEG/metadata.json
Citation (bibtex):
title={Automatic segmentation of blood cells from microscopic slides: a comparative analysis},
author={Depto, Deponker Sarker and Rahman, Shazidur and Hosen, Md Mekayel and Akter, Mst Shapna and Reme, Tamanna Rahman and Rahman, Aimon and Zunair, Hasib and Rahman, M Sohel and Mahdy, MRC},
journal={Tissue and Cell},
volume={73},
pages={101653},
year={2021},
publisher={Elsevier}
}
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[4] BRIFISEG
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License: CC-BY 4.0
Dataset link: https://zenodo.org/records/7195636
Metadata file: Microscopy/BRIFISEG/metadata.json
Citation (bibtex):
@article{mathieu2022brifiseg,
title={Brifiseg: a deep learning-based method for semantic and instance segmentation of nuclei in brightfield images},
author={Mathieu, Gendarme and Bachir, El Debs and others},
journal={arXiv preprint arXiv:2211.03072},
year={2022}
}
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[5] CCAGT
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License: CC-BY-NC 3.0
Dataset link: https://data.mendeley.com/datasets/wg4bpm33hj/2
Metadata file: Microscopy/CCAGT/metadata.json
Citation (bibtex):
@data{Amorim_CCAgT_Images_of_2022,
author = {Atkinson Amorim and João Gustavo and André Matias and Tainee Bottamedi and Vinícius Sanches and Ane Francyne Costa and Fabiana Onofre and Alexandre Onofre and Aldo Wangenheim},
title = {CCAgT: Images of Cervical Cells with AgNOR Stain Technique},
publisher = {Mendeley Data},
year = {2022},
version = {V2},
doi = {10.17632/wg4bpm33hj.2},
url = {https://doi.org/10.17632/wg4bpm33hj.2}
}
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[6] EMDS_6
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License: CC-BY 4.0
Dataset link: https://figshare.com/articles/dataset/EMDS-6/17125025/1?file=31660352
Metadata file: Microscopy/EMDS_6/metadata.json
Citation (bibtex):
@article{Group2021,
author = "MIaMIA Group",
title = "{EMDS-6}",
year = "2021",
month = "12",
url = "https://figshare.com/articles/dataset/EMDS-6/17125025",
doi = "10.6084/m9.figshare.17125025.v1"
}
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[7] FLUORESCENT_NEURONAL_CELLS
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License: CC-BY 4.0
Dataset link: https://amsacta.unibo.it/id/eprint/7347/
Metadata file: Microscopy/FLUORESCENT_NEURONAL_CELLS/metadata.json
Citation (bibtex):
@misc{amsacta7347,
publisher = {University of Bologna},
year = {2024},
title = {Fluorescent Neuronal Cells v2},
url = {https://amsacta.unibo.it/id/eprint/7347/},
abstract = {Fluorescent Neuronal Cells v2 is a collection of fluorescence microscopy images and the corresponding ground-truth annotations, designed to foster innovative research in the domains of Life Science and Deep Learning.
This dataset encompasses three image collections wherein rodent neuronal cell nuclei and cytoplasm are stained with diverse markers to highlight their anatomical or functional characteristics.
Specifically, we release 1874 high-resolution images alongside 750 corresponding ground-truth annotations for several learning tasks, including semantic segmentation, object detection and counting.
The contribution is two-fold.
First, thanks to the variety of annotations and their accessible formats, we envision our work would facilitate methodological advancements in computer vision approaches for segmentation, detection, feature learning, unsupervised and self-supervised learning, transfer learning, and related areas.
Second, by enabling extensive exploration and benchmarking, we hope Fluorescent Neuronal Cells v2 would catalyze breakthroughs in fluorescence microscopy analysis and promote cutting-edge discoveries in life sciences.
For more information, please refer to Clissa, L. et al., 2024. Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for Deep Learning in Microscopy. Scientific data. https://doi.org/10.1038/s41597-024-03005-9.
This research was partly funded by PNRR - M4C2 - Investimento 1.3, Partenariato Esteso PE00000013 - "FAIR - Future Artificial Intelligence Research" - Spoke 8 "Pervasive AI" and the European Commission under the NextGeneration EU programme.
The collection of original images was supported by funding from the University of Bologna and the European Space Agency (Research agreement collaboration 4000123556).},
keywords = {semantic segmentation; object detection; object counting; neuronal cells; fluorescent microscopy},
author = {Clissa, Luca and Occhinegro, Alessandra and Piscitiello, Emiliana and Taddei, Ludovico and Macaluso, Antonio and Morelli, Roberto and Squarcio, Fabio and Hitrec, Timna and Di Cristoforo, Alessia and Luppi, Marco and Amici, Roberto and Cerri, Matteo and Bastianini, Stefano and Berteotti, Chiara and Lo Martire, Viviana and Martelli, Davide and Tupone, Domenico and Zoccoli, Giovanna}
}
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[8] OCCISC
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License: "the database used in this challenge is currently publicly available"
Dataset link: https://cs.adelaide.edu.au/~carneiro/isbi14_challenge/dataset.html
Metadata file: Microscopy/OCCISC/metadata.json
Citation (bibtex):
@article{lu2016evaluation,
title={Evaluation of three algorithms for the segmentation of overlapping cervical cells},
author={Lu, Zhi and Carneiro, Gustavo and Bradley, Andrew P and Ushizima, Daniela and Nosrati, Masoud S and Bianchi, Andrea GC and Carneiro, Claudia M and Hamarneh, Ghassan},
journal={IEEE journal of biomedical and health informatics},
volume={21},
number={2},
pages={441--450},
year={2016},
publisher={IEEE}
}
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[9] PCMMD
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License: CC-BY 4.0
Dataset link: https://data.mendeley.com/datasets/3v2nrxpr9s/1
Metadata file: Microscopy/PCMMD/metadata.json
Citation (bibtex):
@data{Andrade_PCMMD_Plasma_Cells_2024,
author = {Caio Andrade and Marcos Ferreira and Brenno Alencar and Jorge Batista Filho and Matheus Guimarães and Iarley Moraes and Tiago Lopes and Allan dos Santos and Mariane dos Santos and Maria da Silva e Silva and Izabela Rosa and Gilson de Carvalho and Herbert Santos and Marcia Santos and Roberto Meyer and Luciana Knop and Songeli Freire and Ricardo Rios and Tatiane Nogueira},
title = {PCMMD: Plasma Cells for Multiple Myeloma Diagnosis},
year = {2024},
publisher = {Mendeley Data},
version = {V1},
doi = {10.17632/3v2nrxpr9s.1},
url = {https://doi.org/10.17632/3v2nrxpr9s.1}
}
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[10] SLIMIA
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License: CC-BY 4.0
Dataset link: https://figshare.com/collections/The_Spheroid_Light_Microscopy_Image_Atlas_SLiMIA_for_morphometrical_analysis_of_three_dimensional_cell_cultures/7486311
Metadata file: Microscopy/SLIMIA/metadata.json
Citation (bibtex):
@article{blondeel2025spheroid,
title={The Spheroid Light Microscopy Image Atlas for morphometrical analysis of three-dimensional cell cultures},
author={Blondeel, Eva and Peirsman, Arne and Vermeulen, Stephanie and Piccinini, Filippo and De Vuyst, Felix and Est{\^e}v{\~a}o, Diogo and Al-Jamei, Sayida and Bedeschi, Martina and Castellani, Gastone and Cruz, T{\^a}nia and others},
journal={Scientific Data},
volume={12},
number={1},
pages={283},
year={2025},
publisher={Nature Publishing Group UK London}
}
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[11] SSTEM
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License: CC BY-NC-SA 3.0
Dataset link: http://github.com/unidesigner/groundtruth-drosophila-vnc
Metadata file: Microscopy/SSTEM/metadata.json
Citation (bibtex):
@article{gerhard2013segmented,
title={Segmented anisotropic ssTEM dataset of neural tissue},
author={Gerhard, Stephan and Funke, Jan and Martel, Julien and Cardona, Albert and Fetter, Richard},
journal={(No Title)},
year={2013},
publisher={figshare}
}
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[12] VICAR
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License: CC-BY 4.0
Dataset link: https://zenodo.org/records/5153251#.YlAixn9Bzmg
Metadata file: Microscopy/VICAR/metadata.json
Citation (bibtex):
@article{vicar2021self,
title={Self-supervised pretraining for transferable quantitative phase image cell segmentation},
author={Vicar, Tomas and Chmelik, Jiri and Jakubicek, Roman and Chmelikova, Larisa and Gumulec, Jaromir and Balvan, Jan and Provaznik, Ivo and Kolar, Radim},
journal={Biomedical optics express},
volume={12},
number={10},
pages={6514--6528},
year={2021},
publisher={Optical Society of America}
}
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[13] WBC
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License: GPL-3.0
Dataset link: https://github.com/zxaoyou/segmentation_WBC
Metadata file: Microscopy/WBC/metadata.json
Citation (bibtex):
@article{Zheng2018,
title={Fast and Robust Segmentation of White Blood Cell Images by Self-supervised Learning},
author={Xin Zheng and Yong Wang and Guoyou Wang and Jianguo Liu},
journal={Micron},
volume={107},
pages={55--71},
year={2018},
publisher={Elsevier}
doi={https://doi.org/10.1016/j.micron.2018.01.010},
url={https://www.sciencedirect.com/science/article/pii/S0968432817303037}
}
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[14] YEAZ
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License: CC-BY 4.0
Dataset link: https://www.epfl.ch/labs/lpbs/data-and-software/#:~:text=Nature%20Methods%20(2024)-,YeaZ,-%E2%80%93%20A%20convolutional%20neural
Metadata file: Microscopy/YEAZ/metadata.json
Citation (bibtex):
@article{dietler2020convolutional,
title={A convolutional neural network segments yeast microscopy images with high accuracy},
author={Dietler, Nicola and Minder, Matthias and Gligorovski, Vojislav and Economou, Augoustina Maria and Joly, Denis Alain Henri Lucien and Sadeghi, Ahmad and Chan, Chun Hei Michael and Kozi{\'n}ski, Mateusz and Weigert, Martin and Bitbol, Anne-Florence and others},
journal={Nature communications},
volume={11},
number={1},
pages={5723},
year={2020},
publisher={Nature Publishing Group UK London}
}
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IMPORTANT NOTES
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- 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
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