---
pretty_name: SynthMT
annotations_creators:
- no-annotation
language:
- en
license: cc-by-4.0
tags:
- microscopy
- synthetic-data
- microtubules
- irm
- tirf
- biology
- instance-segmentation
- computer-vision
- foundation-models
- segmentation
task_categories:
- image-segmentation
task_ids:
- instance-segmentation
size_categories:
- 1K<n<10K
dataset_info:
  features:
  - name: id
    dtype: string
  - name: image
    dtype: image
  - name: mask
    list: image
  splits:
  - name: train
    num_bytes: 3984597522
    num_examples: 6600
  download_size: 3963881685
  dataset_size: 3984597522
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# SynthMT: A Synthetic Benchmark for Automated Microtubule Segmentation

- **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*](https://doi.org/10.1371/journal.pcbi.1013901)  
**Dataset:** https://huggingface.co/datasets/HTW-KI-Werkstatt/SynthMT

_`*Equal contribution`_

## 🧬 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:

- Realistic **synthetic MT images**  
- Pixel-perfect **instance segmentation labels**  
- 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 **human-grade performance** on unseen real data.

---

## 🖼 Example from the Dataset

<div align="center">

<table>
<tr>
<td align="center"><b>(a) IRM-like synthetic image</b><br><img src="assets/fig_1_image.png" width="95%"></td>
<td align="center"><b>(b) Ground-truth instance mask</b><br><img src="assets/fig_1_gt.png" width="95%"></td>
</tr>
<tr>
<td align="center"><b>(c) FIESTA anchor point predictions</b><br><img src="assets/fig_1_fiesta.png" width="95%"></td>
<td align="center"><b>(d) SAM3Text segmentation</b><br><img src="assets/fig_1_sam3text.png" width="95%"></td>
</tr>
</table>

**Overview of SynthMT and example predictions.**

</div>

---

## 📦 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. |
| `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

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.

However:

- Manual MT annotation is time-consuming and unscalable  
- IRM/TIRF imaging varies significantly across labs (domain shift)  
- No large, labeled benchmarks existed for MT segmentation  

**SynthMT** directly addresses this gap.

---

## 📥 Installation & Loading

Install the Hugging Face `datasets` library:

```bash
pip install datasets
````

Load the dataset entirely in memory with masks stacked:

```python
from datasets import load_dataset
import numpy as np

ds = load_dataset("HTW-KI-Werkstatt/SynthMT", split="train")

sample = ds[0]

# 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)
mask_stack = np.stack([np.array(mask.convert("L")) for mask in sample["mask"]], axis=0)
```

No disk I/O is required — everything can be used in-memory.

---

## 🔗 Links

* **Project Page:** [https://datexis.github.io/SynthMT-project-page/](https://datexis.github.io/SynthMT-project-page/)
* **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://doi.org/10.1371/journal.pcbi.1013901](https://doi.org/10.1371/journal.pcbi.1013901)

---

## 📄 Citation

If you use this dataset, please cite:

```bibtex
@article{koddenbrock2026synthetic,
    doi = {10.1371/journal.pcbi.1013901},
    author = {Koddenbrock, Mario AND Westerhoff, Justus AND Fachet, Dominik AND Reber, Simone AND Gers, Felix A. AND Rodner, Erik},
    journal = {PLOS Computational Biology},
    publisher = {Public Library of Science},
    title = {Synthetic data enables human-grade microtubule analysis with foundation models for segmentation},
    year = {2026},
    month = {05},
    volume = {22},
    url = {https://doi.org/10.1371/journal.pcbi.1013901},
    pages = {1-25},
    abstract = {Studying microtubules (MTs) and their mechanical properties is central to understanding intracellular transport, cell division, and drug action. While important, experts still need to spend many hours manually segmenting these filamentous structures. The suitability of state-of-the-art methods for this task cannot be systematically assessed, as large-scale labeled datasets are missing. We address this gap by introducing the synthetic dataset SynthMT, produced by tuning a novel image generation pipeline on real-world interference reflection microscopy (IRM) frames of in vitro reconstituted MTs without requiring human annotations. In our benchmark, we evaluate nine fully automated methods for MT analysis in both zero- and Hyperparameter Optimization (HPO)-based few-shot settings. Across both settings, classical algorithms and current foundation models still struggle to achieve the accuracy required for biological downstream analysis on in vitro MT IRM images that humans perceive as visually simple. However, a notable exception is the recently introduced SAM3 model. After HPO on only ten random SynthMT images, its text-prompted version SAM3Text achieves near-perfect and in some cases super-human performance on unseen, real data. This indicates that fully automated MT segmentation has become feasible when method configuration is effectively guided by synthetic data. To enable progress, we publicly release the dataset, the generation pipeline, and the evaluation framework.},
    number = {5}
}
```

---

## 🏷 License

**CC-BY-4.0** - See [LICENSE](LICENSE) for details.

---

## 🙏 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