---
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](https://huggingface.co/datasets/HTW-KI-Werkstatt/SynthMT), 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:

```bash
pip install datasets
```

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

```python
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}
}
```