Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D
Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D
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DOI：
10.1073/pnas.1804420115
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发表时间：
2018-09-04
影响因子：
11.1
通讯作者：
Lai, Samuel K.
中科院分区：
文献类型：
--
作者：
Newby, Jay M.;Schaefer, Alison M.;Lai, Samuel K.
粒子跟踪是一种强大的生物物理工具，它需要将大视频文件转换为位置时间序列，即，用于数据分析的感兴趣物种的痕迹。目前的跟踪方法，基于一组有限的输入参数来识别明亮的物体，是装备不良，以处理典型的亚微米物种在复杂的生物环境中的时空异质性和低信噪比的频谱。广泛的用户参与往往是必要的，以优化和执行跟踪方法，这不仅是低效的，但引入用户的偏见。为了开发一种完全自动化的跟踪方法，我们开发了一种卷积神经网络，用于从图像数据中定位粒子，包括6,000多个参数，并使用机器学习技术在各种视频条件下训练网络。神经网络跟踪器提供了前所未有的自动化和准确性，在2D和3D模拟视频以及难以跟踪物种的2D实验视频上具有极低的假阳性和假阴性率。
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.