OmniMedSeg-Microscopy

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. 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} } ---------------------------------------- [8] OCCISC ---------------------------------------- License: "the database used in this challenge is currently publicly available" Dataset link: 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} } ---------------------------------------- [9] PCMMD ---------------------------------------- License: CC-BY 4.0 Dataset link: 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 = {doi.org/10.17632/3v2nrxpr9s.1} } ---------------------------------------- [10] SLIMIA ---------------------------------------- License: CC-BY 4.0 Dataset link: 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} } ---------------------------------------- [11] SSTEM ---------------------------------------- License: CC BY-NC-SA 3.0 Dataset link: 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} } ---------------------------------------- [12] VICAR ---------------------------------------- License: CC-BY 4.0 Dataset link: 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} } ---------------------------------------- [13] WBC ---------------------------------------- License: GPL-3.0 Dataset link: 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={doi.org/10.1016/j.micron.2018.01.010}, url={sciencedirect.com/science/article/pii/S0968432817303037} } ---------------------------------------- [14] YEAZ ---------------------------------------- License: CC-BY 4.0 Dataset link: 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} } ---------------------------------------- ================================================================================ 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