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Add LICENSE file and update README with dataset details and installation instructions

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LICENSE ADDED
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README.md CHANGED
@@ -1,4 +1,27 @@
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  dataset_info:
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  features:
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  - name: id
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  - name: image
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  dtype: image
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  - name: mask
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- list: image
 
 
 
 
 
 
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  splits:
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  - name: train
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- num_bytes: 3063820
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- num_examples: 5
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- download_size: 3049613
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- dataset_size: 3063820
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  configs:
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  - config_name: default
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  data_files:
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  - split: train
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  path: data/train-*
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ pretty_name: SynthMT
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+ annotations_creators:
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+ - no-annotation
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+ language:
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+ - en
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+ license: cc-by-4.0
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+ tags:
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+ - microscopy
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+ - synthetic-data
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+ - microtubules
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+ - irm
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+ - tirf
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+ - biology
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+ - instance-segmentation
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+ - computer-vision
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+ - foundation-models
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+ - segmentation
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+ task_categories:
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+ - image-segmentation
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+ task_ids:
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+ - instance-segmentation
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+ size_categories:
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+ - around_10k
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  dataset_info:
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  features:
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  - name: id
 
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  - name: image
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  dtype: image
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  - name: mask
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+ dtype:
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+ array3_d:
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+ shape:
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+ - null
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+ - 512
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+ - 512
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+ dtype: uint16
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  splits:
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  - name: train
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+ num_bytes: 43191602168
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+ num_examples: 6600
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+ download_size: 3943294163
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+ dataset_size: 43191602168
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  configs:
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  - config_name: default
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  data_files:
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  - split: train
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  path: data/train-*
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  ---
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+
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+
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+ # SynthMT: A Synthetic Benchmark for Automated Microtubule Segmentation
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+
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+ **Authors:** Mario Koddenbrock*, Justus Westerhoff*, Dominik Fachet, Simone Reber, Felix Gers, Erik Rodner
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+ **Affiliations:** HTW Berlin, BHT Berlin, MPI for Infection Biology
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+ **Code & Pipeline:** https://github.com/ml-lab-htw/SynthMT
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+ **Paper:** *Synthetic Data Enables Human-Grade Microtubule Analysis by Tuning Segmentation Foundation Models*
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+ **Dataset:** https://huggingface.co/datasets/HTW-KI-Werkstatt/SynthMT
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+
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+
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+ _`*Equal contribution`_
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+
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+ # 🧬 Overview
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+
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+ **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.
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+ It provides:
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+
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+ - Realistic **synthetic MT images**
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+ - Pixel-perfect **instance segmentation labels**
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+ - A **label-free generation pipeline** that adapts to any real microscope domain
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+ - A comprehensive benchmark of **classical**, **microscopy-specialized**, and **general-purpose foundation models** (SAM, SAM2, SAM3, SAM3Text)
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+
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+ A core result of the associated paper:
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+ → **SAM3Text**, prompted with *“thin line”* and tuned on **only 10 synthetic images**, achieves **near-human or super-human** segmentation accuracy on unseen real data.
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+
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+ ---
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+
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+ # 🖼 Example from the Dataset (Figure 1)
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+
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+ <div align="center">
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+
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+ <table>
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+ <tr>
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+ <td align="center"><b>(a) IRM-like synthetic image</b><br><img src="assets/fig_1_image.png" width="95%"></td>
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+ <td align="center"><b>(b) Ground-truth instance mask</b><br><img src="assets/fig_1_gt.png" width="95%"></td>
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+ </tr>
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+ <tr>
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+ <td align="center"><b>(c) FIESTA anchor point predictions</b><br><img src="assets/fig_1_fiesta.png" width="95%"></td>
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+ <td align="center"><b>(d) SAM3Text segmentation</b><br><img src="assets/fig_1_sam3text.png" width="95%"></td>
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+ </tr>
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+ </table>
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+
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+ **Figure 1 — Overview of SynthMT and example predictions.**
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+
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+ </div>
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+
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+ ---
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+
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+ # 📦 Dataset Structure
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+
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+ Each sample in SynthMT contains:
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+
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+ | Field | Type | Description |
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+ |---|---|---|
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+ | `id` | `string` | Image identifier |
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+ | `image` | `Image` | Synthetic IRM-like image, decoded from a PNG file. |
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+ | `mask` | `Array3D` | Instance-ID mask with shape (C, 512, 512) and `uint16` dtype. `C` is the number of channels (instances). `0` is background. |
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+
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+
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+ Masks use **instance IDs** rather than binary stacks.
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+
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+ ---
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+
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+ # 🧫 Biological Motivation
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+
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+ 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.
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+
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+ However:
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+
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+ - Manual MT annotation is time-consuming and unscalable
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+ - IRM/TIRF imaging varies significantly across labs (domain shift)
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+ - No large, labeled benchmarks existed for MT segmentation
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+
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+ **SynthMT** directly addresses this gap.
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+
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+ ---
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+
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+ # 📥 Installation & Loading
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+
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+ To load the dataset, first install the Hugging Face `datasets` library:
132
+
133
+ ```bash
134
+ pip install datasets
135
+ ```
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+
137
+ Then, use the following Python code:
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+
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+ ```python
140
+ from datasets import load_dataset
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+
142
+ ds = load_dataset("HTW-KI-Werkstatt/SynthMT")
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+
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+ sample = ds["train"][0]
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+ sample["image"].show()
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+ sample["mask"].show()
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+ ````
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+
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+ ---
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+
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+ # 🔗 Links
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+
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+ * **Dataset:** [https://huggingface.co/datasets/HTW-KI-Werkstatt/SynthMT](https://huggingface.co/datasets/HTW-KI-Werkstatt/SynthMT)
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+ * **Code & Generation Pipeline:** [https://github.com/ml-lab-htw/SynthMT](https://github.com/ml-lab-htw/SynthMT)
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+ * **Paper:** available soon
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+
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+ ---
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+
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+ # 📄 Citation
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+
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+ If you use SynthMT, please cite:
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+
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+ ```bibtex
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+ @article{tba,
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+ title={TBA},
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+ author={TBA},
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+ journal={arXiv},
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+ year={2026}
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+ }
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+ ```
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+
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+ ---
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+
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+ # 🏷 License
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+
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+ **CC-BY-4.0** - See [LICENSE](LICENSE) for details.
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+
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+ ---
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+
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+ # 🙏 Acknowledgements
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+
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+ We thank the researchers and microscopy experts involved in evaluating the perceptual realism and biological plausibility of SynthMT.
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+
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+ ---
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+
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+