Data Synthesis Improves 3D Myotube Instance Segmentation

TL;DR

This paper presents a geometry-driven synthesis pipeline for generating realistic 3D myotube images, enabling effective instance segmentation with limited real annotated data.

Contribution

The authors introduce a novel synthetic data generation method based on biophysical modeling and CycleGAN domain adaptation for 3D myotube segmentation.

Findings

Synthetic data training outperforms existing zero-shot models.

The proposed method achieves a mean IPQ of 0.22 on real data.

Biophysics-driven synthesis enhances segmentation in annotation-scarce domains.

Abstract

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 U-Net with self-supervised encoder pretraining, trained exclusively on synthetic data, achieves a mean IPQ of 0.22 on real data, significantly outperforming three established zero-shot segmentation models, demonstrating that biophysics-driven synthesis enables effective instance segmentation in annotation-scarce biomedical domains.