Update README.md

README.md

@@ -47,6 +47,7 @@ configs:
 
 **Authors:** Mario Koddenbrock*, Justus Westerhoff*, Dominik Fachet, Simone Reber, Felix Gers, Erik Rodner  
 **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
@@ -55,16 +56,16 @@ _`*Equal contribution`_
 
 # 🧬 Overview
 
-**SynthMT** is a synthetic, expert-validated dataset designed to benchmark segmentation models on *in vitro* microtubule (MT) images recorded in interference reflection microscopy (IRM)–like conditions.  
+**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:
 
 - Realistic **synthetic MT images**  
 - Pixel-perfect **instance segmentation labels**  
-- A **label-free generation pipeline** that adapts to any real microscope domain  
-- A comprehensive benchmark of **classical**, **microscopy-specialized**, and **general-purpose foundation models** (SAM, SAM2, SAM3, SAM3Text)
+- A **generation pipeline** that adapts to any real microscope domain **without the need for ground-truth annotations**
+- A comprehensive benchmark of **classical** (FIESTA), **microscopy-specialized** (StarDist, TARDIS, µSAM, CellSAM, Cellpose-SAM), and **general-purpose foundation models** (SAM, SAM2, SAM3, SAM3Text)
 
 A core result of the associated paper:  
-→ **SAM3Text**, prompted with *“thin line”* and tuned on **only 10 synthetic images**, achieves **near-human or super-human** segmentation accuracy on unseen real data.
+→ **SAM3Text**, prompted with “*thin line*” and tuned on **only 10 synthetic images**, achieves **human-grade performance** on unseen real data.
 
 ---
 
@@ -146,21 +147,17 @@ No disk I/O is required — everything can be used in-memory.
 
 # 🔗 Links
 
-* **Dataset:** [https://huggingface.co/datasets/HTW-KI-Werkstatt/SynthMT](https://huggingface.co/datasets/HTW-KI-Werkstatt/SynthMT)
-* **Code & Generation Pipeline:** [https://github.com/ml-lab-htw/SynthMT](https://github.com/ml-lab-htw/SynthMT)
-* **Paper:** available soon
+* **Project Page:** https://datexis.github.io/SynthMT-project-page/
+* **Dataset:** https://huggingface.co/datasets/HTW-KI-Werkstatt/SynthMT
+* **Code & Generation Pipeline:** https://github.com/ml-lab-htw/SynthMT
+* **Paper:** TBA
 
 ---
 
 # 📄 Citation
 
 ```bibtex
-@article{tba,
-  title={TBA},
-  author={TBA},
-  journal={arXiv},
-  year={2026}
-}
+TBA
 ```
 
 ---
@@ -173,7 +170,12 @@ No disk I/O is required — everything can be used in-memory.
 
 # 🙏 Acknowledgements
 
-We thank the researchers and microscopy experts involved in evaluating the perceptual realism and biological plausibility of SynthMT.
+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
 
 ```