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Training and test images of live S. aureus cells for the task of cell segmentation. Additional information can be found in the github wiki. The example shows the bright field and Nile Red fluorescence image of live S. aureus cells, as well as the manually annotated segmentation mask. Data type: Paired DIC/fluorescence and segmented mask images Microscopy data type: 2D widefield images (DIC and fluorescence) Microscope: GE HealthCare Deltavision OMX system (with temperature and humidity control, 37��C) equipped with an Olympus 60x 1.42NA Oil immersion objective and 2 PCO Edge 5.5 sCMOS cameras (one for DIC, one for fluorescence) Cell type: S. aureus strain JE2 grown under agarose pads File format: .tif (16-bit) Image size: 512 x 512 px�� (80 nm/px) Image preprocessing: Raw images were manually annotated by drawing ellipses in the NR fluorescence image and segmented images were created using the LOCI plugin (���ROI Map���). For training, images and masks were quartered into four 256 x 256 px�� patches. Author(s): Pedro Matos Pereira1,2, Mariana Pinho1,3 Contact email: pmatos@itqb.unl.pt and mgpinho@itqb.unl.pt Affiliation: 1) Bacterial Cell Biology, Instituto de Tecnologia Qu��mica e Biol��gica Ant��nio Xavier, Universidade Nova de Lisboa, Oeiras, Portugal 2) ORCID: https://orcid.org/0000-0002-1426-9540 3) ORCID: https://orcid.org/0000-0002-7132-8842
Segmentation, Deep Learning, StarDist, ZeroCostDL4Mic, S. aureus
Segmentation, Deep Learning, StarDist, ZeroCostDL4Mic, S. aureus
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