--- 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: - around_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 **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 _`*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
(a) IRM-like synthetic image
 (b) Ground-truth instance mask
(c) FIESTA anchor point predictions
 (d) SAM3Text segmentation
** Overview of SynthMT and example predictions.**
--- # 📦 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) 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/) * **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:** [https://www.biorxiv.org/content/10.64898/2026.01.09.698597v2](https://www.biorxiv.org/content/10.64898/2026.01.09.698597v2) --- # 📄 Citation ```bibtex @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} } ``` --- # 🏷 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