A python package for background and shading correction of optical microscopy images
Project description
BaSiCPy
A python package for background and shading correction of optical microscopy images
BaSiCPy is a python package for background and shading correction of optical microscopy images. It is developed based on the Matlab version of BaSiC tool.
Reference: A BaSiC Tool for Background and Shading Correction of Optical Microscopy Images
by Tingying Peng, Kurt Thorn, Timm Schroeder, Lichao Wang, Fabian J Theis, Carsten Marr*, Nassir Navab*, Nature Communication 8:14836 (2017). doi: 10.1038/ncomms14836.
Simple examples
Notebook | Description | Colab Link |
---|---|---|
timelapse_brightfield | 100 continuous brightfield frames of a time-lapse movie of differentiating mouse hematopoietic stem cells. | |
timelapse_nanog | 189 continuous fluorescence frames of a time-lapse movie of differentiating mouse embryonic stem cells, which move much more slower compared to the fast moving hematopoietic stem cells, resulting in a much larger correlation between frames. Note that in this challenging case, the automatic parameters are no longer optimal, so we use the manual parameter setting (larger smooth regularization on both flat-field and dark-field) to improve BaSiC’s performance. | |
WSI_brain | you can stitch image tiles together to view the effect of shading correction |
You can also find examples of running the package at folder Notebooks. Data used in the examples and a description can be downloaded from Zenodo.
Installation
Download from PyPI
pip install 'PACKAGE-NAME'
or install latest development version
git clone https://github.com/peng-lab/BaSiCPy.git
cd BaSiCPy
pip install .
Recommended: use virtual environment
$ cd BaSiCPy
$ python -m venv venv
$ source venv/bin/activate
(venv) $ pip install -e .
Install with dev dependencies
git clone https://github.com/peng-lab/BaSiCPy.git
cd BaSiCPy
python -m venv venv
source venv/bin/activate
pip install -e '.[dev]'
Development
bump2version
This repository uses bump2version to manage dependencies. New releases are pushed to PyPi in the CI pipeline when a new version is committed with a version tag and pushed to the repo.
The development flow should use the following process:
- New features and bug fixes should be pushed to
dev
- When tests have passed a new development version is ready to be release, use
bump2version major|minor|patch
. This will commit and create a new version tag with the-dev
suffix. - Additional fixes/features can be added to the current development release by using
bump2version build
. - Once the new bugs/features have been tested and a main release is ready, use
bump2version release
to remove the-dev
suffix.
After creating a new tagged version, push to Github and the version will be built and pushed to PyPi.
Contributors
Current version
Nicholas-Schaub 💻 ⚠️ 👀 🤔 📆 |
Tim Morello 👀 ⚠️ 🤔 💻 |
Tingying Peng 💻 🔣 💵 📢 |
Yohsuke T. Fukai 💻 🔬 💬 ⚠️ 🤔 |
- Nicholas Schaub (@Nicholas-Schaub)
- General mentoring, technology selection and project management
- Designing and implementing core structure of the package
- Code review and advising
- Providing tests
- Tim Morello (@tdmorello)
- Designing and implementing core structure of the package
- Providing tests
- Code review
- Tingying Peng (@tying84)
- General comments and financial support
- Reviewing theoretical calculation
- Providing a JAX implementation for the approximate fitting routine
- Providing test data and commenting on expected output
- Yohsuke T. Fukai (@yfukai)
- Theoretical calculation for the optimization problem
- Implementation of the main fitting routine
- Providing tests
- Code review
Old version (f3fcf19
), used as the reference implementation to check the approximate algorithm
- Lorenz Lamm (@LorenzLamm)
- Mohammad Mirkazemi (@Mirkazemi)
Project details
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