Main Repo - Dataset - Preprint

Studying microtubules (MTs) and their mechanical properties is central to understanding intracellular transport, cell division, and drug action, yet experts still spend many hours manually segmenting these filamentous structures. The suitability of state-of-the-art models for this task cannot be orderly assessed, as large-scale labeled datasets are missing. We address this gap by presenting the synthetic dataset SynthMT, which is the product of tuning a novel image generation pipeline on unlabeled, real-world interference reflection microscopy (IRM) frames of in vitro reconstituted microtubules. In our benchmark, we systematically test nine models in both zero- and Hyperparameter Optimization (HPO)-bbased few-shot settings. Across both, classical and current foundation models still struggle to achieve the accuracy required for biological downstream analysis on, to humans, visually simple in vitro MT IRM images. However, a notable exception is the recently introduced SAM3 model. After HPO on only ten random SynthMT images, its text-prompt version SAM3Text achieves near-perfect and in some cases super-human performance on unseen real data. This result indicates that fully automated MT segmentation has become feasible when model configuration is effectively guided through synthetic data.

This is a static website to showcase the SynthMT dataset and the results of the model evaluation.

As it is static, a simple local server is sufficient to run it:

python -m http.server

• index.html: Main page
• styles.css: Styles
• script.js: JS
• media/: Media for the main page
• data_images/: Dataset images folder (raw and segmented)