Abstract

Quantitative cell colocalization in fluorescence microscopy often depends on ad hoc combinations of image loading, segmentation, region selection, manual inspection, and spreadsheet post-processing. Such workflows are difficult to transfer across projects and often obscure how intermediate results were produced. We present CellColoc, an open-source Python workflow pipeline for segmentation-based cell colocalization, single-channel segmentation, and cell feature extraction in 2D and 3D microscopy images. CellColoc provides a modular workflow layer that integrates existing segmentation backends, including Cellpose and threshold-based methods, into reusable, script-driven analyses. The package supports channel-wise backend selection, interactive or reusable regions of interest, optional third-channel occupancy and cell-positivity analysis, z-cropping and z-projection, cached post hoc refinement of Cellpose thresholds, and reanalysis after manual mask editing. Analyses are executed from concise user scripts while reusable functionality is kept in a core package. Intermediate artifacts such as ROI masks, per-channel label masks, positive-cell masks, and structured result tables are written to a standardized results directory, promoting transparent inspection, reproducibility, and FAIR-aligned reuse. Public example datasets, a synthetic benchmark, and archived software releases accompany the package. By separating reusable analysis logic from project-specific configuration, CellColoc offers an extensible foundation for community-driven microscopy workflows that need transparent per-cell overlap classification, morphology readouts, and reusable batch analysis.