Niels Bohr Institute & Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
Commun Biol. 2023 Jul 19;6(1):754. doi: 10.1038/s42003-023-05098-1.
Computer-aided analysis of biological microscopy data has seen a massive improvement with the utilization of general-purpose deep learning techniques. Yet, in microscopy studies of multi-organism systems, the problem of collision and overlap remains challenging. This is particularly true for systems composed of slender bodies such as swimming nematodes, swimming spermatozoa, or the beating of eukaryotic or prokaryotic flagella. Here, we develop a end-to-end deep learning approach to extract precise shape trajectories of generally motile and overlapping slender bodies. Our method works in low resolution settings where feature keypoints are hard to define and detect. Detection is fast and we demonstrate the ability to track thousands of overlapping organisms simultaneously. While our approach is agnostic to area of application, we present it in the setting of and exemplify its usability on dense experiments of swimming Caenorhabditis elegans. The model training is achieved purely on synthetic data, utilizing a physics-based model for nematode motility, and we demonstrate the model's ability to generalize from simulations to experimental videos.
利用通用的深度学习技术，生物显微镜数据的计算机辅助分析取得了巨大的进展。然而，在多生物体系统的显微镜研究中，碰撞和重叠的问题仍然具有挑战性。对于由游动线虫、游动精子或真核或原核鞭毛的拍打等细长体组成的系统尤其如此。在这里，我们开发了一种端到端的深度学习方法，用于提取一般运动和重叠的细长体的精确形状轨迹。我们的方法适用于特征关键点难以定义和检测的低分辨率设置。检测速度很快，我们展示了同时跟踪数千个重叠生物体的能力。虽然我们的方法不局限于应用领域，但我们将其应用于密集的游动秀丽隐杆线虫实验中，并举例说明了其可用性。模型训练完全基于合成数据，利用基于物理的线虫运动模型，我们证明了模型从模拟到实验视频的泛化能力。
