Datasets:
File size: 3,143 Bytes
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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: 29642168
num_examples: 66
download_size: 29211850
dataset_size: 29642168
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}
}
```
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