NucGen3D: A synthetic framework for large-scale 3D nuclear segmentation with open-source training data and models

Why clinicians should know about it

• Picked for Histology (paper of the day, 6 September 2026): Synthetic 3D nuclear segmentation dataset for microscopy

Abstract

Robust nuclear segmentation in 3D microscopy images is a critical yet unsolved challenge in quantitative cell biology. The main challenges are the scarcity and variability of annotated volumetric datasets. Since these data are difficult to obtain, most state-of-the-art approaches, including Cellpose, segment individual 2D slices and then heuristically reconstruct 3D volumes, thereby losing critical spatial context. Our analysis of expert annotator performance confirms that ignoring 3D context introduces substantial variability in nuclear detection and annotation. While a few 3D models are trained on small or toy datasets, no large-scale, openly available resource currently exists to enable robust training of high-capacity 3D segmentation networks. To address this problem, we present NucGen3D, a customizable simulation framework that generates large-scale, annotated 3D microscopy datasets from limited 2D input, specifically the 2018 Data Science Bowl dataset. NucGen3D produces realistic 3D volumes across diverse biological and imaging scenarios, including variations in nuclear morphology, spatial arrangement, and imaging noise. Using this synthetic data, we trained two models from scratch: a 2D convolutional neural network under Cellpose-like conditions, and a fully 3D convolutional model that extends the 2D settings. We evaluated both on a challenging, independent real-world dataset with complex nuclear architectures. Both models, especially the 3D model, achieve competitive performance with respect to state-of-the-art methods, including those trained on larger annotated datasets or based on more complex architectures. These results demonstrate that synthetic data can effectively substitute for full 3D annotations when training models at scale. To promote reproducibility and further research, we release both the NucGen3D framework and the fully trained 3D segmentation model as open source, making this the first end-to-end open resource for large-scale 3D nuclear segmentation.

Abstract as published, via PubMed.