A label-free method to track individuals and lineages of budding cells

A label-free method to track individuals and lineages of budding cells

Single-cell microscopy data and images with manually curated annotations for "A label-free method to track individuals and lineages of budding cells" by Pietsch, Muñoz, Adjavon, Farquhar, Clark and Swain. The data come from fluorescence microscopy experiments where yeast, with fluorescence...

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Hauptverfasser: Pietsch, Julian M J, Muñoz, Alán F, Adjavon, Diane-Yayra A, Farquhar, Iseabail, Clark, Ivan B N, Swain, Peter S
Format: Dataset
Sprache:eng
Schlagworte:
Biological Sciences::Molecular Biology Biophysics and Biochemistry
budding yeast
Cell cycle
convolutional neural network
deep learning
fluorescence microscopy
growth rate
image analysis
microfluidics
object tracking
Saccharomyces cerevisiae
segmentation
time series
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Zusammenfassung:Single-cell microscopy data and images with manually curated annotations for "A label-free method to track individuals and lineages of budding cells" by Pietsch, Muñoz, Adjavon, Farquhar, Clark and Swain. The data come from fluorescence microscopy experiments where yeast, with fluorescence tags on Sfp1, Nhp6A, or Myo1, were grown in microfluidics devices and subject to changes in nutrients. Cells were segmented and tracked by the BABY algorithm described in the paper. Also included are manually curated image annotations used to train and benchmark both segmentation and tracking performance of the BABY algorithm. Source code for the algorithm is available at https://git.ecdf.ed.ac.uk/swain-lab/baby.
DOI:10.7488/ds/3427