BioSR Dataset (biosr_dataset)

Overview

This DeepTrackAI repository provides a preprocessed part of the BioSR dataset, available from figshare (DOI: 10.6084/m9.figshare.13264793.v9) and originally published by Chang Qiao et al., Nature Methods, 2021.

The original dataset consists of paired low-resolution (LR) and high-resolution (HR) fluorescence microscopy images for training and benchmarking super-resolution reconstruction methods, covering four biology structures (Clatrin Coated Pits, Endoplasmatic Reticulum, Microtubules, F-actin), nine signal levels (15-600 average photon count), and two upscaling-factors (linear SIM and non-linear SIM).

This repo only includes the Microtubules folder and the images in this repository have been cropped into 128 × 128 pixel patches, saved as 32-bit grayscale TIF files and organized into training/validate/test splits to be directly usable in deep learning workflows, while preserving the original content and licensing terms.

Summary

• Number of Image Pairs:
  • Training: 41,040 pairs
  • Validation: 2,160 pairs
  • Test: 150 HR images × 9 LR signal levels (1,350 LR images total). Each subfolder corresponds to a different signal-to-noise level, reflecting increasing average photon counts (15–600 photons).
• Image Size: 128 × 128 pixels
• Format: 32-bit grayscale TIF images

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Original Source

• Title: BioSR: a biological image dataset for super-resolution microscopy
• Authors: Chang Qiao and Di Li
• Source: figshare (DOI: 10.6084/m9.figshare.13264793.v9)
• License: Creative Commons Attribution 4.0 International (CC BY 4.0)

If you use this dataset, please follow the licensing requirements and provide proper attribution to the original authors.

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Dataset Structure

/biosr_dataset
└── BioSR/
    └── Microtubules/
        ├── training_wf/      # Low-resolution training images (TIF)
        │   ├── 00000001.tif
        │   ├── 00000002.tif
        │   └── ...
        ├── training_gt/      # High-resolution training images (TIF)
        │   ├── 00000001.tif
        │   ├── 00000002.tif
        │   └── ...
        ├── validate_wf/      # Low-resolution validation images (TIF)
        │   ├── 00000001.tif
        │   ├── 00000002.tif
        │   └── ...
        ├── validate_gt/      # High-resolution validation images (TIF)
        │   ├── 00000001.tif
        │   ├── 00000002.tif
        │   └── ...
        ├── test_wf/          # Low-resolution test images, 9 signal levels
        │   ├── level_01/     # Lowest photon count (~15), lowest SNR
        │   │   ├── 001.tif
        │   │   ├── 002.tif
        │   │   └── ...
        │   ├── level_02/
        │   │   ├── 001.tif
        │   │   ├── 002.tif
        │   │   └── ...
        │   ├── ...
        │   └── level_09/     # Highest photon count (~600), highest SNR
        └── test_gt/          # High-resolution test images (TIF)
            ├── 001.tif
            ├── 002.tif
            └── ...

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How to Access the Data

Clone the Repository

git clone https://github.com/DeepTrackAI/biosr_dataset
cd biosr_dataset

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Attribution

This replication dataset is based on the original BIOSR dataset. When using this replication, please cite both the dataset and the original paper.

Cite the dataset:

Qiao, Chang; Li, Di. BioSR: a biological image dataset for super-resolution microscopy. (2020) https://doi.org/10.6084/m9.figshare.13264793.v9

@article{Qiao2020,
author = "Chang Qiao and Di Li",
title = "{BioSR: a biological image dataset for super-resolution microscopy}",
year = "2020",
month = "11",
url = "https://figshare.com/articles/dataset/BioSR/13264793",
doi = "10.6084/m9.figshare.13264793.v9"
}

Cite the original paper:

Qiao C, Li Y, Qu J, et al. Evaluation and development of deep neural networks for image super-resolution in optical microscopy. Nature Methods, 18: 194–202 (2021).
https://doi.org/10.1038/s41592-020-01048-5

@article{qiao2021biosr,
  title={Evaluation and development of deep neural networks for image super-resolution in optical microscopy},
  author={Qiao, Chang and Li, Yuxiang and Qu, Junle and others},
  journal={Nature Methods},
  volume={18},
  pages={194--202},
  year={2021},
  publisher={Nature Publishing Group},
  doi={10.1038/s41592-020-01048-5}
}

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License

This dataset is shared under the Creative Commons Attribution 4.0 International (CC BY 4.0) License, following the original licensing terms.