BaSiCPy: Scalable and Robust Shading Correction for Optical Microscopy Images
Liu, Y.; Fukai, Y. T.; Cano-Muniz, S.; Perez, V.; Todorov, M.; Ortega, G.; Morello, T.; Loeffler, D.; Paetzold, J.; Xu, X.; Lamm, L.; Ma, N.; Erturk, A.; Schroeder, T.; Boeck, L.; Schapiro, D.; Schaub, N.; Marr, C.; Peng, T.
Show abstract
Quantitative fluorescence microscopy is frequently confounded by spatially varying illumination and temporal intensity drift. Although BaSiC is a widely adopted retrospective correction method, it can fail when foreground content is strongly correlated across images, a common regime in time-lapse, tiled and volumetric acquisitions, and its application often requires manual parameter tuning that limits reproducibility and scalability. We introduce BaSiCPy, a foreground-aware implementation of BaSiC that improves illumination profile estimation under correlated foreground structures, provides automatic hyperparameter selection and accelerates large-scale processing through GPU support. BaSiCPy is distributed as an open-source Python package with graphical and programmatic interfaces, facilitating integration into contemporary bioimage analysis workflows.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.