Virtual Staining Dataset (vs_dataset)

Overview

This DeepTrackAI repository replicates part of the In Silico Labeling Dataset, available from the in-silico-labeling GitHub Repository and described in Christiansen et al., Cell, 2018.

These images were used for developing models that predict virtual staining of biological samples from brightfield images.

From the original dataset, this repository includes only the folders named Rubin/scott_1_0, corresponding to human motor neurons (Condition A).

Each field of view contains:

• Brightfield images: a z-stack of 13 images acquired at different focal planes (RGB, identical content in all three channels).
• Fluorescence images: spatially coregistered with the brightfield images, showing:
  • Hoechst stain — nuclei (blue)
  • Anti-TuJ1 stain — neurons (green)
• Predicted fluorescence images: generated by virtual staining models.

Summary

• Number of fields of view: 25 (22 for training, 3 for testing)
• Per field of view: 13 brightfield images + 1 fluorescence image + 1 predicted fluorescence image
• Image format: PNG, RGB, 8-bit per channel
• Image size: various, depending on acquisition

The filenames contain metadata sufficient to identify the image contents.

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Original Source

• Title: In Silico Labeling Dataset
• Authors: Original authors listed in the paper
• Source: in-silico-labeling GitHub Repository
• License: Creative Commons Attribution 4.0 International (CC BY 4.0)

If you use this dataset in your research, you must follow the licensing requirements and properly attribute the original authors.

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Dataset Structure

/vs_dataset  
  ├── train/          # Training images
  └── test/           # Test images        

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How to Access the Data

Clone the Repository

git clone -b virtual_staining_dataset github.com/DeepTrackAI/vs_dataset
cd virtual_staining_dataset

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Attribution

This replication dataset is based on the original In Silico Labeling Dataset. When using this replication, please cite the original paper.

Cite the original paper:

Christiansen E, Yang S, Ando D, Javaherian A, Skibinski G, Lipnick S, Mount E, O'Neil A, Shah K, Lee A, Goyal P, Fedus W, Poplin R, Esteva A, Berndl M, Rubin L, Nelson P, Finkbeiner S. In silico labeling: Predicting fluorescent labels in unlabeled images. Cell, 173(3): 792–803 (2018). https://doi.org/10.1016/j.cell.2018.03.040

@article{christiansen2018isl,
  title={In silico labeling: predicting fluorescent labels in unlabeled images},
  author={Christiansen, Eric M and Yang, Samuel J and Ando, D Michael and Javaherian, Ashkan and Skibinski, Gaia and Lipnick, Scott and Mount, Elliot and O’Neil, Alison and Shah, Kevan and Lee, Alicia K and Goyal, Piyush and Fedus, William and Poplin, Ryan and Esteva, Andre and Berndl, Marc and Rubin, Lee L and Nelson, Philip and Finkbeiner, Steven},
  journal={Cell},
  volume={173},
  number={3},
  pages={792--803},
  year={2018},
  publisher={Elsevier}
}

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License

This replication dataset is shared under the Creative Commons Attribution 4.0 International License, following the original licensing terms.