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

Abstract

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 a modular family of geometry-preserving alignment problems. ZenReg combines Fourier phase correlation, intensity-based StackReg-style alignment, NoRMCorre-style piecewise translation fields, projection-based rotation estimates, dense SimpleITK-based six-degree-of-freedom volume registration, and sparse point-based rigid-volume registration within one canonical microscopy stack model. The platform uses OMIO to normalize heterogeneous microscope files and to preserve metadata, while optional disk-backed Zarr arrays support chunked, memory-efficient processing of image stacks that exceed available memory or reside on remote storage. ZenReg writes 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, while high-noise and large-drift tests separated the backend behavior: FFT-based methods failed abruptly once image information or shared support became insufficient, StackReg degraded more gradually under severe noise, and piecewise NoRMCorre improved spatially varying local-motion correction where a single global transform was inadequate. Parallel execution reduced runtime for large time series, and ZenReg provided practical full-volume rigid correction for dense and puncta-rich 3D+t stacks.

By coupling a modular, extensible backend architecture to transparent sidecar outputs, ZenReg makes motion correction easier to extend, inspect, share, reproduce, and reuse as part of scientific image analysis.