DeepBacs – E. coli SIM prediction dataset and CARE model
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下载链接：
https://zenodo.org/record/5551152
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资源简介：
Training and test images of live, membrane-labeled E. coli cells for prediction of SIM super-resolution images from widefield images, as well as a trained CARE model.
Additional information can be found on this github wiki.
The example image shows a widefield fluorescence image and SIM reconstruction of FM5-95 labelled, live E. coli cells.
Training and test dataset
Data type: Paired microscopy images (fluorescence) of low (widefield) and high resolution (SIM)
Microscopy data type: Fluorescence microscopy (FM5-95)
Microscope: GE HealthCare Deltavision OMX system (with temperature and humidity control, 37°C) equipped with an Olympus 60x 1.42NA Oil immersion objective and 2 PCO Edge 5.5 sCMOS cameras (one for DIC, one for fluorescence)
Cell type: E. coli DH5α grown under agarose pads
File format: .tif (16-bit for widefield images and 32-bit for SIM reconstructions)
Image size: 1024 x 1024 px² (40 nm/px)
Image preprocessing: E. coli widefield images were scaled with a factor of 2 to match the SIM reconstruction pixel size.
CARE model
The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 300 epochs on 5500 paired image patches (image dimensions: (1024 x 1024 px²), patch size: (80 x 80 px²), 100 patches/image) with a batch size of 8 and a laplace loss function, using the CARE 2D ZeroCostDL4Mic notebook (v 1.12). Key python packages used include tensorflow (v 0.1.12), Keras (v2.3.1), csbdeep (v 0.6.1), numpy (v1.19.5), cuda (v 10.1.243). The training was accelerated using a Tesla P100GPU and data was augmented by a factor of 4 using rotation and flipping.
Model weights can be used with the ZeroCostDL4Mic CARE 2D notebook or the CSBDeep Fiji plugin.
Author(s): Pedro Matos Pereira1,2, Mariana Pinho1,3
Contact email: pmatos@itqb.unl.pt and mgpinho@itqb.unl.pt
Affiliation:
1) Bacterial Cell Biology, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
2) ORCID: https://orcid.org/0000-0002-1426-9540
3) ORCID: https://orcid.org/0000-0002-7132-8842
创建时间：
2024-07-17
