BaSiCPy 0.2.0.dev2

A python package for background and shading correction of optical microscopy images

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.

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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:

1. New features and bug fixes should be pushed to dev
2. 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.
3. Additional fixes/features can be added to the current development release by using bump2version build.
4. 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)