Abstract
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.
Abstract (translated)
细胞追踪在生物医学研究中仍然是一个关键但具有挑战性的任务。由于深度学习在为此目的的全面且多样化的训练数据集的可用性方面往往被低估，因此深度学习在此任务上的全部潜力常常未被充分利用。在本文中，我们提出了SynCellFactory，一种生成细胞视频的增强方法。SynCellFactory的核心是ControlNet架构，该架构已通过在风格和运动模式上合成细胞图像来提高其准确性。这种技术能够创建与真实显微镜时间间隔复杂性相仿的合成细胞视频。我们的实验结果表明，SynCellFactory能够显著提高已有的深度学习模型在细胞追踪方面的性能，特别是当原始训练数据稀疏时。
URL
https://arxiv.org/abs/2404.16421
