用物理驱动的合成数据，解决肌肉纤维3D分割缺乏标注的问题。
Data Synthesis Improves 3D Myotube Instance Segmentation
- 基于真实显微图像构建多项式中心线与分叉结构的合成模型。
- 仅用合成数据训练的3D U-Net在真实数据上达0.22平均实例分割质量。
- 适合缺乏标注数据的生物医学图像分割任务，尤其肌管研究者。
肌管是多核肌纤维，是研究肌肉生理、疾病机制和药物反应的关键模型。相关机制研究与药物筛选依赖于直径、长度、分支程度等定量形态学指标，而这需要精确的三维实例分割。然而，由于缺乏大规模标注的肌管数据集，现有预训练生物医学分割模型难以泛化。本文提出一种几何驱动的合成流程，通过多项式中心线、局部变化半径、分叉结构及椭球端帽模拟真实显微观察中的肌管形态。合成体数据加入真实噪声、光学伪影，并通过CycleGAN实现域适应。采用自监督编码器预训练的紧凑型3D U-Net，仅在合成数据上训练，即在真实数据上取得0.22的平均实例分割质量（IPQ），显著优于三种零样本分割模型，证明了生物物理驱动合成可有效支持标注稀缺领域的实例分割。
原文摘要 · Abstract (English)
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.
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