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Name: ZeroCostDL4Mic - Stardist example training and test dataset (see our Wiki for details) Data type: Paired microscopy images (fluorescence) and corresponding masks Microscopy data type: Fluorescence microscopy (SiR-DNA) and masks obtained via manual segmentation (see https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist for details about the segmentation) Microscope: Spinning disk confocal microscope with a 20x 0.8 NA objective Cell type: DCIS.COM LifeAct-RFP cells File format: .tif (16-bit for fluorescence and 8 and 16-bit for the masks) Image size: 1024x1024 (Pixel size: 634 nm) Author(s): Johanna Jukkala1,2 and Guillaume Jacquemet1,2 Contact email: guillaume.jacquemet@abo.fi Affiliation : 1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland 2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland Associated publications: Unpublished Funding bodies: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.
Stardist, Nuclei segmentation, Image segmentation, Microscopy, Deep learning
Stardist, Nuclei segmentation, Image segmentation, Microscopy, Deep learning
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