bioRxiv · 10.64898/2026.04.28.721386
BaSiCPy: Scalable and Robust Shading Correction for Optical Microscopy Images
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.
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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.. 2026-05-01. BaSiCPy: Scalable and Robust Shading Correction for Optical Microscopy Images. https://doi.org/10.64898/2026.04.28.721386
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