minor changes

README.md

@@ -21,7 +21,7 @@ task_categories:
 task_ids:
 - instance-segmentation
 size_categories:
-- around_10k
+- 1K<n<10K
 dataset_info:
   features:
   - name: id
@@ -45,17 +45,16 @@ configs:
 
 # SynthMT: A Synthetic Benchmark for Automated Microtubule Segmentation
 
-**Authors:** Mario Koddenbrock*, Justus Westerhoff*, Dominik Fachet, Simone Reber, Felix Gers, Erik Rodner
-**Affiliations:** HTW Berlin, BHT Berlin, MPI for Infection Biology  
+- **Authors:** Mario Koddenbrock*, Justus Westerhoff*, Dominik Fachet, Simone Reber, Felix Gers, Erik Rodner
+  - *Equal contribution
+- **Affiliations:** HTW Berlin, BHT Berlin, MPI for Infection Biology  
 
-**Project Page:** https://datexis.github.io/SynthMT-project-page/
-**Code & Pipeline:** https://github.com/ml-lab-htw/SynthMT  
-**Paper:** *Synthetic Data Enables Human-Grade Microtubule Analysis with Foundation Models for Segmentation*  
-**Dataset:** https://huggingface.co/datasets/HTW-KI-Werkstatt/SynthMT
+- **Project Page:** https://datexis.github.io/SynthMT-project-page/
+- **Code & Pipeline:** https://github.com/ml-lab-htw/SynthMT  
+- **Paper:** [*Synthetic Data Enables Human-Grade Microtubule Analysis with Foundation Models for Segmentation*](https://www.biorxiv.org/content/10.64898/2026.01.09.698597v2)
 
-_`*Equal contribution`_
 
-# 🧬 Overview
+## 🧬 Overview
 
 **SynthMT** is a synthetic, expert-validated dataset designed to benchmark segmentation models on *in vitro* microtubule (MT) images visualized in interference reflection microscopy (IRM)–like conditions.  
 It provides:
@@ -70,7 +69,7 @@ A core result of the associated paper:
 
 ---
 
-# 🖼 Example from the Dataset
+## 🖼 Example from the Dataset
 
 <div align="center">
 
@@ -85,25 +84,25 @@ A core result of the associated paper:
 </tr>
 </table>
 
-** Overview of SynthMT and example predictions.**
+**Overview of SynthMT and example predictions.**
 
 </div>
 
 ---
 
-# 📦 Dataset Structure
+## 📦 Dataset Structure
 
 Each sample in SynthMT contains:
 
 | Field | Type | Description |
 |---|---|---|
 | `id` | `string` | Unique image identifier |
-| `image` | `Image` | Synthetic IRM-like image, decoded from PNG. Can be converted to a numpy array `(H, W, 3)` for in-memory processing. |
+| `image` | `Image` | Synthetic IRM-like image, decoded from PNG. Can be converted to a numpy array `(512, 512, 3)` for in-memory processing. |
 | `mask` | `Array3D` | **Stack of instance masks** with shape `(C, 512, 512)` and `uint16` dtype, where `C` = number of instances in the image. Background pixels = 0. Stored in the dataset as a `Sequence(Image())` but can be stacked in memory for in-memory pipelines. |
 
 ---
 
-# 🧫 Biological Motivation
+## 🧫 Biological Motivation
 
 Microtubules (MTs) are cytoskeletal filaments essential for intracellular transport, cell motility, and mitotic spindle formation. Measuring MT **count**, **length**, and **curvature** is critical for *in vitro* reconstitution experiments, drug discovery, and mechanistic cell biology.
 
@@ -117,7 +116,7 @@ However:
 
 ---
 
-# 📥 Installation & Loading
+## 📥 Installation & Loading
 
 Install the Hugging Face `datasets` library:
 
@@ -135,7 +134,7 @@ ds = load_dataset("HTW-KI-Werkstatt/SynthMT", split="train")
 
 sample = ds[0]
 
-# Image as numpy array (H, W, 3)
+# Image as numpy array (H, W, 3) = (512, 512, 3)
 img_array = np.array(sample["image"].convert("RGB"))
 
 # Masks as stacked numpy array (C, H, W)
@@ -146,16 +145,19 @@ No disk I/O is required — everything can be used in-memory.
 
 ---
 
-# 🔗 Links
+## 🔗 Links
 
 * **Project Page:** [https://datexis.github.io/SynthMT-project-page/](https://datexis.github.io/SynthMT-project-page/)
-* **Dataset:** [https://huggingface.co/datasets/HTW-KI-Werkstatt/SynthMT](https://huggingface.co/datasets/HTW-KI-Werkstatt/SynthMT)
+* **This Dataset:** [https://huggingface.co/datasets/HTW-KI-Werkstatt/SynthMT](https://huggingface.co/datasets/HTW-KI-Werkstatt/SynthMT)
+* **Real IRM in-vitro MT dataset (also used in our paper):** [https://huggingface.co/datasets/HTW-KI-Werkstatt/IRM-in-vitro-microtubules](https://huggingface.co/datasets/HTW-KI-Werkstatt/IRM-in-vitro-microtubules)
 * **Code & Generation Pipeline:** [https://github.com/ml-lab-htw/SynthMT](https://github.com/ml-lab-htw/SynthMT)
 * **Paper:** [https://www.biorxiv.org/content/10.64898/2026.01.09.698597v2](https://www.biorxiv.org/content/10.64898/2026.01.09.698597v2)
 
 ---
 
-# 📄 Citation
+## 📄 Citation
+
+If you use this dataset, please cite:
 
 ```bibtex
 @article{koddenbrock2026synthetic,
@@ -173,19 +175,17 @@ No disk I/O is required — everything can be used in-memory.
 
 ---
 
-# 🏷 License
+## 🏷 License
 
 **CC-BY-4.0** - See [LICENSE](LICENSE) for details.
 
 ---
 
-# 🙏 Acknowledgements
+## 🙏 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
-
-
+and his lab for helpful discussions