Synthetic data enables human-grade microtubule analysis with foundation models for segmentation
Fig 2
Our synthetic data generation pipeline produces realistic microtubule (MT) images with corresponding instance segmentation masks conditioned on a parameter set .
(1) Generating geometry creates instance masks from geometric parameters (count, length, curvature) using polylines. (2.1) Physical rendering applies point-spread function (PSF) convolution to replicate optical properties, and adds red seeds and uniform background. (2.2) Artifact simulation introduces realistic distractor features (circular spots, irregular structures). (2.3) Noise addition models signal-dependent (Poisson) and signal-independent (Gaussian) noise sources. (2.4) Global distortions apply spatially-varying effects (vignetting, blur, contrast variations) to match real microscopy conditions. This approach enables the generation of labeled data that closely approximates experimental interference reflection microscopy (IRM) images, when its set of generation parameters is tuned accordingly (as explained in section 4).