CSBDeep – a toolbox for CARE¶
This is the documentation for the CSBDeep Python package, which provides a toolbox for content-aware restoration (CARE) of (fluorescence) microscopy images, based on deep learning via Keras and TensorFlow. Please see the CSBDeep website for more information with links to our manuscript and supplementary material. If you use this software for your research, please cite:
Content-Aware Image Restoration: Pushing the Limits of Fluorescence Microscopy. Martin Weigert, Uwe Schmidt, Tobias Boothe, Andreas Müller, Alexandr Dibrov, Akanksha Jain, Benjamin Wilhelm, Deborah Schmidt, Coleman Broaddus, Siân Culley, Mauricio Rocha-Martins, Fabián Segovia-Miranda, Caren Norden, Ricardo Henriques, Marino Zerial, Michele Solimena, Jochen Rink, Pavel Tomancak, Loic Royer, Florian Jug, and Eugene W. Myers. Nature Methods 15.12 (2018): 1090–1097.
This is an early version of the software. Several features are still missing, including the use of network ensembles and creating training data via simulation.
Table of contents¶
- Model overview
- Training data generation
- Model training
- Model application