With the increasing complexity and throughput of microscopy experiments, it has become essential for biologists to navigate computational means of analysis to produce automated and reproducible workflows. Bioimage analysis workflows being largely underreported in method sections of articles, it is however quite difficult to find practical examples of documented scripts to support beginner programmers in biology. Here, we introduce COverlap, a Fiji toolset composed of four macros, for the 3D segmentation and colocalization of fluorescent nuclear markers in confocal images. The toolset accepts batches of multichannel z-stack images, segments objects in two channels of interest, and outputs object counts and labels, as well as co-localization results based on the physical overlap of objects. The first macro is a preparatory step that produces maximum intensity projections of images for visualization purposes. The second macro assists users in selecting batch-suitable segmentation parameters by testing them on small portions of the images. The third macro performs automated segmentation and colocalization analysis, and saves the parameters used, the results table, the 3D regions of interest (ROIs) of co-localizing objects, and two types of verification images with segmentation and co-localization masks for each image of the batch. The fourth macro allows users to review the verification images displaying segmentation masks and the location of co-localization events, and to perform corrections such as ROI adjustment, z-stack reslicing, and volume estimation correction in an automatically documented manner. To illustrate how COverlap operates, we present an experiment in which we identified rare endothelial proliferation events in adult rat brain slices on more than 350 large tiled z-stacks. We conclude by discussing the reproducibility and generalizability of the toolset, its limitations for different datasets, and its potential use as a template that is adaptable to other types of analyses.< Réduire