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Cytoland

Robust virtual staining of landmark organelles from label-free microscopy.

Cytoland virtual staining of nuclei and membrane from label-free phase contrast

Part of the VisCy monorepo.

Paper: Robust virtual staining in Nature Machine Intelligence

Installation

# From the VisCy monorepo root
uv pip install -e "applications/cytoland"

Usage

Training and prediction use the shared viscy CLI provided by viscy-utils:

# Training (pick a model-specific config)
uv run --package cytoland viscy fit -c examples/configs/vscyto3d/finetune.yml

# Training with Spotlight loss
uv run --package cytoland viscy fit -c examples/configs/vscyto3d/train_spotlight.yml

# Prediction
uv run --package cytoland viscy predict -c examples/configs/vscyto3d/predict.yml

The YAML config determines which model and data module to use via class_path:

model:
  class_path: cytoland.engine.VSUNet
data:
  class_path: viscy_data.hcs.HCSDataModule

Tutorials and demos

Scripts and tutorials live under examples/:

Folder What it demonstrates
examples/VS_model_inference/ Python API inference demos for VSCyto2D, VSCyto3D, VSNeuromast, and TTA-augmented sliding-window prediction
examples/vcp_tutorials/ Virtual Cell Platform quick-start and organism-specific walkthroughs (HEK293T, neuromast)
examples/dl-course-exercise/ Image-translation course exercise (training from scratch + evaluation) — used at DL@MBL and DL@Janelia
examples/phase_contrast/ Phase-contrast tutorial and demo workflows
examples/configs/ YAML configs for viscy fit / viscy predict across models (VSCyto2D/3D, VSNeuromast, FNet3D, dynacell)

All demo scripts are written as jupytext-style percent-cell .py files. Regenerate paired .ipynb notebooks with jupytext --to ipynb solution.py if you prefer the notebook UI.

Models

Model Input Output Architecture
VSCyto3D Phase3D Nuclei + Membrane FCMAE / UNeXt2
VSCyto2D Phase2D Nuclei + Membrane UNeXt2
VSNeuromast DIC Multiple fluorescent markers UNeXt2
FNet3D Transmitted light Fluorescence Unet3d (Ounkomol et al. 2018)

FNet3D note: All spatial dimensions (Z, Y, X) must be divisible by 2^depth (default depth=4 requires divisibility by 16). See examples/configs/fnet3d/fit.yml.

Benchmark note: FNet3D and SEC61B benchmarks now launch from applications/dynacell/. Cytoland copies are transitional legacy — see examples/configs/dynacell/ and examples/configs/fnet3d/.

References

Liu, Hirata-Miyasaki et al., 2025 
@article{liu2025robust,
    title = {Robust virtual staining of landmark organelles},
    author = {Liu, Ziwen and Hirata-Miyasaki, Eduardo and Pradeep, Soorya and others},
    journal = {Nature Machine Intelligence},
    year = {2025},
    doi = {10.1038/s42256-025-01046-2},
}

Project links

Data verified by PyPI on Aug 19, 2026
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  • Released:
Newer release available (0.1.0)

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Avatar for MehtaLab from gravatar.com MehtaLab

Credits

Author: Biohub

GitHub Statistics

Data verified by PyPI on Aug 19, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

BSD-3-Clause
View SPDX License List

Requires

Python >=3.12

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Tags

deep learning fluorescence prediction microscopy virtual staining

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