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HTW-KI-Werkstatt/SynthMT

371 downloads in 30 days1 likesLast modified 6 May 2026View on Hugging Face

Permissive or share-alike

The declared licence (cc-by-4.0) is classified as permissive.

Declared licence: cc-by-4.0, from the tagEvidence: README.md at 1b5b928
Licence files: LICENSE, detected as cc-by-4.0Evidence: LICENSE at 1b5b928
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Tasks

image-segmentation

Citation text from the card

@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 analy

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dataset_licence({ "id": "HTW-KI-Werkstatt/SynthMT" })
signal: "permissive"
stated_licence: "cc-by-4.0"
sources: 0