Update README.md

README.md

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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-*
----
+---
+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}
+}
+```
+