NucLogic: Logic-based geometric 3D nuclear segmentation for fast and robust analysis of challenging biological imaging data
Year: 2026
Type: preprint
Abstract: Nuclear segmentation in 3D microscopy remains challenging, particularly for low signal-to-noise ratio (SNR) data from dense tissue samples, timelapse imaging, or in vivo experiments. Yet accurate 3D nuclear segmentation is a prerequisite for quantitative single-cell analysis, making this a key bottleneck for high-throughput study of challenging datasets. We present NucLogic, a fast and robust 3D nuclear segmentation algorithm that combines the maturity of 2D segmentation with geometric stitching rules based on nuclear shape, orientation, and intensity, to reconstruct accurate 3D volumes. NucLogic reliably segments challenging datasets including long-term timelapse imaging of organoids, and large dense structures such as the zebrafish brain. Benchmarked against other state-of-the-art segmentation tools, NucLogic matches F1 scores at high SNR, with increasing outperformance as SNR decreases. Importantly, segmentation completes approximately 3-to 5-fold faster, with greater speed gains on larger images. Beyond segmentation, NucLogic includes an intuitive graphical user interface allowing non-experts to segment, quantify, and visualize 3D and 4D data. This enables straightforward quantification of cell numbers, alongside integration of a wide range of both cell-intrinsic properties, such as phenotype and morphology, and local environment metrics, such as density and spatial interactions. Together, NucLogic makes quantitative 3D nuclear analysis of previously unmanageable datasets accessible to a broad research community.
Institution: Utrecht University
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: green
Funder: Universiteit Utrecht
OpenAlex ID: W7220741718