Cell tracking remains a pivotal yet challenging task in biomedical research. The full potential of deep learning for this purpose is often untapped due to the limited availability of comprehensive and varied training data sets. In this paper, we present SynCellFactory, a generative cell video augmentation. At the heart of SynCellFactory lies the ControlNet architecture, which has been fine-tuned to synthesize cell imagery with photorealistic accuracy in style and motion patterns. This technique enables the creation of synthetic yet realistic cell videos that mirror the complexity of authentic microscopy time-lapses. Our experiments demonstrate that SynCellFactory boosts the performance of well-established deep learning models for cell tracking, particularly when original training data is sparse.

翻译：细胞追踪仍然是生物医学研究中关键但具有挑战性的任务。由于缺乏全面且多样化的训练数据集，深度学习在此方面的潜力通常未能充分发挥。本文提出了SynCellFactory，一种生成式细胞视频增强方法。其核心在于ControlNet架构，该架构经过精细调优，能够生成在风格和运动模式上具有照片级逼真度的细胞图像。该技术可创建合成但真实的细胞视频，模拟真实显微镜延时摄影的复杂性。实验表明，SynCellFactory能够提升现有深度学习模型在细胞追踪任务中的性能，尤其是在原始训练数据稀疏的情况下。