Datasets:

HTW-KI-Werkstatt
/

IRM-in-vitro-microtubules

license: cc-by-4.0
dataset_info:
  features:
    - name: id
      dtype: string
    - name: image
      dtype: image
    - name: mask
      list: image
  splits:
    - name: train
      num_bytes: 29640052
      num_examples: 66
  download_size: 29211448
  dataset_size: 29640052
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

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 SynthMT synthetic dataset, enabling seamless switching between real and synthetic data for benchmarking and model development.

Data type: Real in vitro IRM images
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 (H, W, 3))
`mask`	Array3D	Instance masks, same as SynthMT (C, H, W)

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"))
# If masks are present:
# 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://www.biorxiv.org/content/10.64898/2026.01.09.698597v2

License

CC-BY-4.0

Citation

If you use this dataset, please cite:

@article{koddenbrock2026synthetic,
    author = {Koddenbrock, Mario and Westerhoff, Justus and Fachet, Dominik and Reber, Simone and Gers, Felix A. and Rodner, Erik},
    title = {Synthetic data enables human-grade microtubule analysis with foundation models for segmentation},
    elocation-id = {2026.01.09.698597},
    year = {2026},
    doi = {10.64898/2026.01.09.698597},
    publisher = {Cold Spring Harbor Laboratory},
    URL = {https://www.biorxiv.org/content/early/2026/01/12/2026.01.09.698597},
    eprint = {https://www.biorxiv.org/content/early/2026/01/12/2026.01.09.698597.full.pdf},
    journal = {bioRxiv}
}