Annotated images from yeast cell lifespans - Training & Test sets - DetecDiv (id01)

This dataset has been generated by manual annotation from timelapse images of yeast cells dividing using the DetecDiv software (see below).

It contains ~250 000 images from 250 cellular lifespans (each lifespan is made of between 700 and 1000 images). Each image is classified between 6 classes: "1. unbudded", "2. small", "3. large", "4. dead", "5. empty", "6. clog", according to the subfolder of the image.

Besides, this folder also contains a .mat file containing 250 timeseries of classes corresponding to the lifespan of the 200 cells.

The dataset used for training (200 cellular lifespans) is in the folder /trainingset, while the dataset used for testing (50cellular lifespans) is in the folder /testset

It has been used to train the network doi.org/10.5281/zenodo.5553862 from the software DetecDiv: github.com/gcharvin/DetecDiv

biorxiv.org/content/10.1101/2021.10.05.463175v1

Data type: 3D microscopy images (3 stacks brightfield) (.tif) + annotation (.mat)

Microscopy data type: Brightfield images with 3 stacks, each stack representing a color of RGB.

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: 60x60x1 (Pixel size: x,y: 325 nm, 3*z: 3*1325 nm)

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.