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SynthMT

Synthetic and real IRM microtubule images with instance masks

A synthetic microtubule image, its ground-truth instance masks, and predictions by FIESTA and SAM3Text

A synthetic benchmark for microtubule segmentation with pixel-perfect instance masks, plus real IRM images in the same format, from our paper in PLOS Computational Biology.

Two datasets for benchmarking microtubule segmentation in interference reflection microscopy (IRM) images of in vitro reconstituted microtubules. Both use the same format, so you can switch between synthetic and real data by changing only the dataset name in your code.

SynthMT holds 6,600 synthetic IRM-like images (512 × 512 px) with a pixel-perfect instance mask for every microtubule. Our generation pipeline was tuned on unlabeled real microscopy images, without any human annotations, so that the synthetic images match the real ones.

IRM in vitro microtubules holds 66 real IRM images from Simone Reber's lab with instance masks, for evaluating models on real data.

In our paper, SAM3Text, prompted with "thin line" and tuned on only ten SynthMT images, reaches human-grade performance on unseen real data. The preview shows a synthetic image, its ground-truth masks, and the predictions of FIESTA and SAM3Text.

from datasets import load_dataset

synthetic = load_dataset("HTW-KI-Werkstatt/SynthMT", split="train")
real = load_dataset("HTW-KI-Werkstatt/IRM-in-vitro-microtubules", split="train")

Both datasets are released under CC BY 4.0.

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