This dataset has been generated by manual segmentation from timelapse images of yeast cells dividing using the DetecDiv software (see below). It contains ~1950 raw images (folder "images") associated with their mask (folder "labels") with 3 classes of pixels according to their value on the mask image, "1. background", "2. mother", "3. other". A splitSets.mat indicates which images have been used for training or for testing. It is related to the trained network doi.org/10.5281/zenodo.5553851 from the software DetecDiv: github.com/gcharvin/DetecDiv biorxiv.org/content/10.1101/2021.10.05.463175v1 ------------------------------------------------------- Data type: 2D microscopy images (brightfield) (.tif) + mask image (.tif) Microscopy data type: Brightfield images Imaging: 20x 0.45 NA brightfield, 6.5µm*6.5µm sCMOS Cell type: Budding yeast wild type cell (BY4742) File format: .tif (16-bit RGB, 1 color per z-stack) + .mat Image size: Brightfield: 60x60x1 (Pixel size: x,y: 325 nm) // Mask: 60x60x1 (Pixel size: x,y: 325 nm), 3 pixel values (one per class). Author(s): Théo, ASPERT Contact email: theo.aspert@gmail.com Affiliation: IGBMC, Université de Strasbourg Funding bodies: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.

{"references": ["DetecDiv, a deep-learning platform for automated cell division tracking and replicative lifespan analysis Theo Aspert, Didier Hentsch, Gilles Charvin bioRxiv 2021.10.05.463175; doi: https://doi.org/10.1101/2021.10.05.463175", "ASPERT Th\u00e9o. (2021). Trained network for segmentation of yeast cell from brightfield images in microfluidic traps - DetecDiv (id02) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5553851"]}