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CellSeg3D, Self-supervised 3D cell segmentation for fluorescence microscopy
Cyril Achard1, Timokleia Kousi1, Markus Frey1
1Brain Mind Institute and Neuro X, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland.
Elife
|June 24, 2025
Summary
A new self-supervised learning method, WNet3D, accurately quantifies cell nuclei in 3D volumes without needing ground truth labels. This advances 3D cell structure analysis, especially in neuroscience, even with limited data.
Area of Science:
- Cell biology
- Neuroscience
- Bioimaging
- Machine learning
Background:
- Accurate 3D cell structure analysis is vital for biology and neuroscience.
- Generating 3D ground truth data for cell quantification is complex and data-intensive.
- Existing methods struggle with label-scarce biological contexts.
Purpose of the Study:
- To develop novel 3D models and a self-supervised learning method for cell nuclei quantification.
- To create accessible tools (CellSeg3D package) for 3D cell segmentation.
- To establish robust benchmarks using diverse 3D datasets.
Main Methods:
- Introduced a 3D transformer (SwinUNetR) and a 3D self-supervised learning method (WNet3D).
- Developed the CellSeg3D Python package with Jupyter Notebook and napari GUI plugin integration.
- Created a human-annotated mesoSPIM dataset for benchmarking.
- Benchmarked WNet3D against state-of-the-art supervised methods on four diverse 3D datasets.
Main Results:
- The self-supervised WNet3D model achieved performance comparable to supervised methods.
- WNet3D demonstrated effectiveness across varied datasets, including challenging, densely packed nuclei.
- CellSeg3D provides accessible tools for 3D cell segmentation.
Conclusions:
- WNet3D offers a powerful solution for 3D cell nuclei quantification in label-scarce scenarios.
- The CellSeg3D package democratizes access to advanced 3D segmentation tools.
- This work facilitates broader applications of 3D cell analysis in biological research.
