DeepBacs – Bacillus subtilis fluorescence segmentation dataset
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https://zenodo.org/record/5550967
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资源简介：
Training and test images of live B. subtilis cells expressing FtsZ-GFP for the task of segmentation.
Additional information can be found on this github wiki.
The example shows the fluorescence widefield image of live B. subtilis cells expressing FtsZ-GFP and the manually annotated segmentation mask.
Data type: Paired fluorescence and segmented mask images
Microscopy data type: 2D widefield images (fluorescence)
Microscope: Custom-built 100x inverted microscope bearing a 100x TIRF objective (Nikon CFI Apochromat TIRF 100XC Oil); images were captured on a Prime BSI sCMOS camera (Teledyne Photometrics)
Cell type: B. subtilis strain SH130 grown under agarose pads
File format: .tiff (8-bit) or .png (8-bit)
For segmented masks, binary masks are used for training of CARE/U-Net models, 8-bit .tif ROI maps for training of StarDist models and .png images for training of pix2pix models
Image size: 1024 x 1024 px² (Pixel size: 65 nm)
Image preprocessing: Images were denoised using PureDenoise and resulting 32-bit images were converted into 8-bit images after normalizing to 1% and 99.98% percentiles. Images were manually annotated using the Labkit Fiji plugin
Author(s): Mia Conduit1,2, Séamus Holden1,3
Contact email: Seamus.Holden@newcastle.ac.uk
Affiliation:
1) Centre for Bacterial Cell Biology, Biosciences Institute, Newcastle University, NE2 4AX UK
2) ORCID: 0000-0002-7169-907X
Associated publications: Whitley et al., 2021, Nature Communications, https://doi.org/10.15252/embj.201696235
创建时间：
2024-07-17
