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Synthetic data enables human-grade microtubule analysis with foundation models for segmentation

Fig 3

Optimizing aligns synthetic image distributions with real, annotation-free microscopy data.

Real interference reflection microscopy (IRM) images (left) and synthetic images (center) are embedded using DINOv2. The parametric generator (right) creates images by sampling from distributions governing geometric properties (filament count, length, curvature) and imaging characteristics (PSF, noise, artifacts, contrast, distortions), all controlled by . An optimization loop iteratively refines by maximizing cosine similarity between real and synthetic embeddings, ensuring that synthetic images match the statistical properties and visual characteristics of experimental data.

doi: https://doi.org/10.1371/journal.pcbi.1013901.g003