Data Synthesis Improves 3D Myotube Instance Segmentation

This is the authors' abstract. We don't add key points for this paper.

Myotubes are multinucleated muscle fibers serving as key model systems for studying muscle physiology, disease mechanisms, and drug responses. Mechanistic studies and drug screening thereby rely on quantitative morphological readouts such as diameter, length, and branching degree, which in turn require precise three-dimensional instance segmentation. Yet established pretrained biomedical segmentation models fail to generalize to this domain due to the absence of large annotated myotube datasets. We introduce a geometry-driven synthesis pipeline that models individual myotubes via polynomial centerlines, locally varying radii, branching structures, and ellipsoidal end caps derived from real microscopy observations. Synthetic volumes are rendered with realistic noise, optical artifacts, and CycleGAN-based Domain Adaptation (DA). A compact 3D UNet with selfsupervised encoder pretraining, trained exclusively on synthetic data, achieves a mean Injective Panoptic Quality (IPQ) of 0.22 on real data, significantly outperforming three established zero-shot segmentation models, demonstrating that geometry-driven synthesis enables effective instance segmentation in annotation-scarce biomedical domains.