Department of Mathematics, University of Alberta, Edmonton, AB, Canada T6G 2R3.
Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599.
Proc Natl Acad Sci U S A. 2018 Sep 4;115(36):9026-9031. doi: 10.1073/pnas.1804420115. Epub 2018 Aug 22.
Particle tracking is a powerful biophysical tool that requires conversion of large video files into position time series, i.e., traces of the species of interest for data analysis. Current tracking methods, based on a limited set of input parameters to identify bright objects, are ill-equipped to handle the spectrum of spatiotemporal heterogeneity and poor signal-to-noise ratios typically presented by submicron species in complex biological environments. Extensive user involvement is frequently necessary to optimize and execute tracking methods, which is not only inefficient but introduces user bias. To develop a fully automated tracking method, we developed a convolutional neural network for particle localization from image data, comprising over 6,000 parameters, and used machine learning techniques to train the network on a diverse portfolio of video conditions. The neural network tracker provides unprecedented automation and accuracy, with exceptionally low false positive and false negative rates on both 2D and 3D simulated videos and 2D experimental videos of difficult-to-track species.
粒子追踪是一种强大的生物物理工具，需要将大型视频文件转换为位置时间序列，即分析数据中感兴趣物种的轨迹。当前的跟踪方法基于一组有限的输入参数来识别明亮的物体，无法处理复杂生物环境中小于微米的物种通常呈现的时空异质性和低信噪比范围。为了优化和执行跟踪方法，通常需要大量用户参与，这不仅效率低下，而且会引入用户偏差。为了开发一种完全自动化的跟踪方法，我们开发了一种用于从图像数据中定位粒子的卷积神经网络，包含超过 6000 个参数，并使用机器学习技术在多样化的视频条件下对网络进行训练。神经网络跟踪器提供了前所未有的自动化和准确性，在二维和三维模拟视频以及难以跟踪物种的二维实验视频上，具有极低的假阳性和假阴性率。
