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M. Fuhrmann

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Open access Aug 2026

ZenReg: A modular Python platform for fast and memory-efficient N-dimensional microscopy image registration

Motion artifacts are almost unavoidable in functional time-lapse and structural volumetric multiphoton microscopy. They arise from respiration, heartbeat, locomotion, awake behavior, instrument heating, mechanical vibration, and slow drift, while the recorded signal is often photon-limited, blurred by scattering, and biologically time varying. Consequently, motion correction is frequently an essential prerequisite for quantitative bioimage analysis rather than a merely cosmetic preprocessing operation. Edge- and landmark-centric registration strategies are often poorly matched to these data because useful structures may be sparse, diffuse, out-of-focus, or changing in fluorescence intensity. We present ZenReg, an open-source Python platform that formulates common 2D+t, 3D, and 3D+t microscopy registration tasks as modular, geometry-preserving alignment problems. ZenReg combines FFT- and intensity-based translational registration, projection-based rotation estimation, piecewise translational motion correction, and dense or sparse six-degree-of-freedom volume registration within one canonical microscopy stack model. Disk-backed arrays support chunked processing of large or remote image stacks, and every run can produce registered images together with shift tables, correlation metrics, summary plots, and machine-readable settings. In synthetic benchmarks with known ground truth, ZenReg recovered global 2D and 3D translations with subpixel accuracy across moderate noise and drift regimes. High-noise and large-drift tests separated the registration models: FFT-based methods failed abruptly once image information or shared support became insufficient, intensity-based translational alignment degraded more gradually under severe noise, and piecewise translational correction improved spatially varying local motion where a single global transform was inadequate. In real biological data, ZenReg increased mean template correlation in a 3000-frame calcium-imaging movie from 0.334 to 0.487 and recovered imposed continuous three-photon volume motion with a mean translational error of 0.055 px or, for six-degree-of-freedom rigid motion, a mean shift error of 0.068 px and a mean rotation error of 0.015 degrees. By coupling modular registration backends to transparent sidecar outputs, ZenReg turns motion correction into an inspectable, memory-aware, and FAIR-oriented component of reproducible bioimage analysis.

Fabrizio Musacchio, M. Fuhrmann · 1 citation
Open access Jul 2026

CellColoc: A modular, open-source workflow for cell colocalization, segmentation, and feature extraction in microscopy images

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.

Fabrizio Musacchio, Henrike Antony, Arush Baijal et al. · 1 citation