DeepBacs – Artificial labeling of E. coli membranes dataset and fnet/CARE models

Training and test images of <em>E. coli </em>cells for artificial labeling of membranes in brightfield images using fnet or CARE, as well as trained models for prediction of super-resolution membranes. Additional information can be found on this github wiki. Example image shows an <em>E. coli</em> bright field image and PAINT membrane image predicted by the neural network (scale bar is 1 µm). <strong>Training and testing dataset</strong> <strong>Data type</strong>: Paired bright field and super-resolution images <strong>Microscopy data type</strong>: Bright field and fluorescence microscopy (widefield and point accumulation for imaging in nanoscale topography (PAINT) images) <strong>Microscope</strong>: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective <strong>Cell type</strong>: <em>E. coli </em>K12 strain derivatives <strong>File format</strong>: .tif (8-bit) <strong>Image size</strong>: 512x512 px² with different pixel sizes: 1x tube lens: 158 nm (raw) and 19.75 nm (8x upscaled for PAINT images) 1.5x tube lens: 106 nm (raw) (widefield fluorescence only) <strong>fnet model (PAINT membrane images)</strong> The fnet 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained for 200,000 steps on 33 paired images (image dimensions: (512 x 512 px²), patch size: (128 x 128 px²)) with a batch size of 4, a learning rate of 0.0004, 10% validation split and 4x data augmentation (flipping and rotation). Model weights can be used with the ZeroCostDL4Mic fnet 2D notebook. <strong>CARE model (PAINT membrane images):</strong> The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained for 300 epochs (100 steps/epoch) on 33 paired images (image dimensions: 512 x 512 px², patch size: 256 x 256 px²) with a batch size of 4, a learning rate of 0.0004, 90/10% train/validation split and 4x data augmentation (flipping and rotation). Model weights can be used with the ZeroCostDL4Mic CARE 2D notebook or the CSBDeep Fiji plugin. <br> <strong>Author(s)</strong>: Christoph Spahn^1,2, Mike Heilemann^1,3 <strong>Contact email</strong>: [e-mail] <strong>Affiliation(s)</strong>: 1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany 2) ORCID: 0000-0001-9886-2263 3) ORCID: 0000-0002-9821-3578