Artificial neural networks for high performance, fully automated particle tracking analysis even at low signal-to-noise regimes

人工神经网络即使在低信噪比条件下也能实现高性能、全自动粒子跟踪分析

• 批准号: 9347679
• 负责人: Samuel Lai
• 金额: $ 22.5万
• 依托单位: AI TRACKING SOLUTIONS, LLC
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-01 至 2019-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9347679
• 关键词: Adopted; Advanced Development; Algorithms; Artificial Intelligence; Automobile Driving; Bacteria; Binding; Biological; Biological Neural Networks; Biological Sciences; Classification; Cloud Computing; Complex; Computer software; Computers; Data; Data Analyses; Development; Diffuse; Ensure; Environment; Eye; Future; Gaussian model; Generations; Goals; Heterogeneity; Human; Image; Image Analysis; Intervention; Knowledge; Laboratories; Lead; Life; Link; Liquid substance; Location; Machine Learning; Manuals; Measurement; Methods; Microscopy; Modeling; Morphologic artifacts; Motion; Noise; Performance; Phase; Photobleaching; Process; Property; Radial; Research; Research Personnel; Retina; Savings; Scientist; Signal Transduction; Small Business Technology Transfer Research; Software Tools; Spottings; Students; Techniques; Technology; Testing; Time; Training; Variant; Virus; Vision; Visual Cortex; Work; base; biophysical tools; cell motility; cloud based; cost; design; experimental study; feeding; field study; graduate student; improved; insight; interest; macromolecule; movie; nanoparticle; novel strategies; particle; pathogen; physical science; response; spatiotemporal; submicron; terabyte; tool; virtual

Abstract: Particle tracking (PT) is a powerful biophysical tool for elucidating molecular interactions, transport phenomena and rheological properties in complex biological environments. Unfortunately, PT remains a niche tool in life and physical sciences with a limited user base, in large part due to significant time and technical constraints in extracting accurate time-variant positional data from recorded movies. These constraints are exacerbated in experiments with low signal-to-noise ratios or substantial heterogeneity, as frequently encountered with nanoparticles and pathogens in biological fluids. Currently available software that attempts to automate the movie analysis process rely almost exclusively on assigning static image filters based on specific intensity, pixel size and signal-to-noise ratio thresholds. Unfortunately, when applied to actual experimental data with substantial spatial and temporal heterogeneity, the current software generally produces substantial numbers of false positives (i.e. tracking artifacts) or false negatives (i.e. missing actual traces), and frequently both. Frequent user intervention is thus required to ensure accurate tracking even when using sophisticated tracking software, markedly reducing experimental throughput and resulting in substantial user- to-user variations in analyzed data. The time required for accurate particle tracking analysis makes PT experiments exceedingly expensive compared to other commonly used experimental techniques in life sciences. These same tracking analysis limitations have effectively precluded investigators from undertaking more sophisticated 3D PT, even though the microscopy capability to obtain such movies is readily available and critical scientific insights can be gained from 3D PT. To circumvent the challenges with currently available particle tracking software, we have developed a new approach for particle identification and tracking, based on machine learning and convolutional neural networks (CNN). CNN is a type of feed-forward artificial neural network designed to process information in a layered network of connections that mimics the organization of real neural networks in the mammalian retina and visual cortex. Unlike most CNN imaging models that are trained to make predictions on static images, we have trained our CNN to input adjacent frames so that each prediction includes information from the past and future, thus effectively performing convolutions in both space and time to infer particle locations. Similar principles of image analysis are now being harnessed by developers of autonomous vehicle technologies to distinguish the motions of different objects on the road. We have applied our CNN tracking algorithm to a wide range of 2D movies capturing dynamic motions of nanoparticles, viruses and highly motile bacteria, achieving at least 30-fold time savings with virtually no need for human intervention while maintaining robust tracking performance (i.e. low false positive and low false negative rates). In this STTR proposal, we seek to focus on further optimization and testing of our neural network tracking platform for 2D PT, including the use of cloud computing (Aim 1), and extending our neural network tracker to enable accurate 3D PT (Aim 2). Our vision is to popularize PT as a research tool among researchers by minimizing the time and labor costs associated with PT analysis.

摘要：粒子追踪是一种强有力的生物物理学工具，用于研究分子间的相互作用、输运 复杂生物环境中的现象和流变特性。不幸的是，PT仍然是一个利基市场， 生命科学和物理科学领域的一个工具，用户基础有限，这在很大程度上是由于大量的时间和技术问题。 从记录的电影中提取准确的时变位置数据的约束。这些约束 在低信噪比或实质异质性的实验中加剧， 在生物液体中遇到纳米粒子和病原体。目前可用的软件， 为了使电影分析过程自动化，几乎完全依赖于根据以下内容分配静态图像过滤器 特定强度、像素大小和信噪比阈值。不幸的是，当应用于实际 实验数据与大量的空间和时间异质性，目前的软件一般产生 大量的假阳性（即跟踪伪影）或假阴性（即丢失实际迹线），以及 经常两者。因此，需要频繁的用户干预，以确保即使在使用 复杂的跟踪软件，大大降低了实验吞吐量，并导致大量的用户- 分析数据中的用户差异。精确的颗粒跟踪分析所需的时间使得PT 与生活中其他常用的实验技术相比， 以理工科为重这些相同的跟踪分析限制有效地阻止了调查人员进行 更复杂的3D PT，即使获得这种电影的显微镜能力是现成的 从3D PT中可以获得重要的科学见解。为了规避现有的挑战， 粒子跟踪软件，我们已经开发了一种新的方法，用于粒子识别和跟踪，基于 机器学习和卷积神经网络（CNN）。CNN是一种前馈人工神经网络 一种网络，用于在分层连接网络中处理信息，该网络模仿 哺乳动物视网膜和视觉皮层中的真实的神经网络。与大多数CNN成像模型不同， 训练对静态图像进行预测，我们训练CNN输入相邻帧， 预测包括来自过去和未来的信息，从而有效地在两个空间中执行卷积 和时间来推断粒子的位置。类似的图像分析原理现在正被 自动驾驶汽车技术的开发人员，以区分道路上不同物体的运动。我们 已经将我们的CNN跟踪算法应用于广泛的2D电影，捕捉 纳米颗粒、病毒和高运动性细菌，节省至少30倍的时间， 同时保持鲁棒的跟踪性能（即，低假阳性和低假阳性 负利率）。在这个STTR提案中，我们寻求专注于进一步优化和测试我们的神经网络。 2D PT的网络跟踪平台，包括使用云计算（Aim 1），并扩展我们的神经网络 网络跟踪器可实现准确的3D PT（目标2）。我们的愿景是将PT作为一种研究工具在 最大限度地减少与PT分析相关的时间和人力成本。

项目成果

Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D

• DOI: 10.1073/pnas.1804420115
• 发表时间: 2018-09-04
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Newby, Jay M.;Schaefer, Alison M.;Lai, Samuel K.
• 通讯作者: Lai, Samuel K.