HTW-KI-Werkstatt/IRM-in-vitro-microtubules

Real IRM Images of in vitro Microtubules

This dataset contains real interference reflection microscopy (IRM) images of in vitro microtubules. It is provided in the exact same format as the synthetic SynthMT dataset, enabling seamless switching between real and synthetic data for benchmarking and model development.

• Data type: Real in vitro IRM images containing microtubules
• Format: Identical structure and field names as SynthMT
• Use case: Benchmarking segmentation models, domain adaptation, and biological analysis

Biological Context

Microtubules are cytoskeletal filaments essential for cell biology. IRM enables label-free imaging of microtubules in vitro, providing high-contrast images for quantitative analysis.

Dataset Structure

Each sample contains:

Field	Type	Description
id	string	Unique image identifier
image	Image	Real IRM image (PNG, can be loaded as (512, 512, 3))
mask	Array3D	Instance masks, same as SynthMT, i.e., with shape (C, 512, 512) and uint16 dtype, where C = number of instances in the image. Background pixels = 0.

The structure matches SynthMT, so you can switch the repo key in your code without changes.

Usage Example

Install the Hugging Face datasets library:

pip install datasets

Load the dataset (just change the repo key from SynthMT):

from datasets import load_dataset
import numpy as np

ds = load_dataset("HTW-KI-Werkstatt/IRM-in-vitro-microtubules", split="train")

sample = ds[0]
img_array = np.array(sample["image"].convert("RGB"))
mask_stack = np.stack([np.array(mask.convert("L")) for mask in sample["mask"]], axis=0)

Related Resources

• Synthetic Dataset (SynthMT): https://huggingface.co/datasets/HTW-KI-Werkstatt/SynthMT
• Project Page: https://datexis.github.io/SynthMT-project-page/
• Paper: https://doi.org/10.1371/journal.pcbi.1013901

License

CC-BY-4.0

📄 Citation

If you use this dataset, please cite:

@article{koddenbrock2026synthetic,
    doi = {10.1371/journal.pcbi.1013901},
    author = {Koddenbrock, Mario AND Westerhoff, Justus AND Fachet, Dominik AND Reber, Simone AND Gers, Felix A. AND Rodner, Erik},
    journal = {PLOS Computational Biology},
    publisher = {Public Library of Science},
    title = {Synthetic data enables human-grade microtubule analysis with foundation models for segmentation},
    year = {2026},
    month = {05},
    volume = {22},
    url = {https://doi.org/10.1371/journal.pcbi.1013901},
    pages = {1-25},
    abstract = {Studying microtubules (MTs) and their mechanical properties is central to understanding intracellular transport, cell division, and drug action. While important, experts still need to spend many hours manually segmenting these filamentous structures. The suitability of state-of-the-art methods for this task cannot be systematically assessed, as large-scale labeled datasets are missing. We address this gap by introducing the synthetic dataset SynthMT, produced by tuning a novel image generation pipeline on real-world interference reflection microscopy (IRM) frames of in vitro reconstituted MTs without requiring human annotations. In our benchmark, we evaluate nine fully automated methods for MT analysis in both zero- and Hyperparameter Optimization (HPO)-based few-shot settings. Across both settings, classical algorithms and current foundation models still struggle to achieve the accuracy required for biological downstream analysis on in vitro MT IRM images that humans perceive as visually simple. However, a notable exception is the recently introduced SAM3 model. After HPO on only ten random SynthMT images, its text-prompted version SAM3Text achieves near-perfect and in some cases super-human performance on unseen, real data. This indicates that fully automated MT segmentation has become feasible when method configuration is effectively guided by synthetic data. To enable progress, we publicly release the dataset, the generation pipeline, and the evaluation framework.},
    number = {5}
}

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🏷 License

CC-BY-4.0 - See LICENSE for details.

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🙏 Acknowledgements

Our work is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project-ID 528483508 - FIP 12. We would like to thank Dominik Fachet and Gil Henkin from the Reber lab for providing data, and also thank the further study participants Moritz Becker, Nathaniel Boateng, and Miguel Aguilar. The Reber lab thanks staff at the Advanced Medical Bioimaging Core Facility (Charité, Berlin) for imaging support and the Max Planck Society for funding. Furthermore, we thank Kristian Hildebrand and Chaitanya A. Athale (IISER Pune, India) and his lab for helpful discussions.