bioRxivJClub
BaSiCPy: Scalable and Robust Shading Correction for Optical Microscopy Images
bioRxiv · · Preprint · Open access
Liu, Fukai + 19 more
Abstract ↗AI summary
The abstract is read at the publisher; the summary is JClub's.
BaSiCPy, a foreground-aware BaSiC implementation for optical fluorescence microscopy, improves illumination profile estimation under strongly correlated foreground structures and adds automatic hyperparameter selection.
- Why it matters: Illumination variation and temporal intensity drift can confound quantitative fluorescence microscopy, and BaSiC can fail when foreground content is strongly correlated across images.
- What they did: BaSiCPy uses a foreground-aware approach to estimate illumination profiles, selects hyperparameters automatically, and accelerates large-scale processing with GPU support.
- The result: The open-source Python package includes graphical and programmatic interfaces, enabling more reproducible, scalable integration into bioimage analysis workflows.
The findingWhy it mattersWhat they didThe result
- Open access