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：粒子跟踪（PT）是阐明分子相互作用的强大生物物理工具 复杂的生物学环境中的现象和流变特性。不幸的是，PT仍然是一个利基 生活和物理科学的工具具有有限的用户群，在很大程度上是由于大量时间和技术 从录制电影中提取准确的时变位置数据时的限制。这些约束是 在低信噪比或实质异质性的实验中加剧 在生物液中遇到纳米颗粒和病原体。当前尝试尝试的软件 自动化电影分析过程几乎完全依赖于基于 特定的强度，像素大小和信噪比阈值。不幸的是，当应用于实际 实验数据具有大量的空间和时间异质性，当前软件通常会产生 大量的误报（即跟踪文物）或假否定性（即缺少实际痕迹），并且 经常两者。因此，需要频繁的用户干预以确保即使使用时准确跟踪 精致的跟踪软件，显着降低了实验吞吐量，并导致了大量的用户 - 分析数据中的使用者变化。精确粒子跟踪分析所需的时间使PT 与生活中其他常用的实验技术相比，实验非常昂贵 科学。这些相同的跟踪分析局限性有效地排除了调查人员的承诺 即使显微镜能够获得此类电影的显微镜能力，也很复杂的3D PT 可以从3D PT获得关键的科学见解。为了解决当前可用的挑战 粒子跟踪软件，我们已经开发了一种基于粒子识别和跟踪的新方法 机器学习和卷积神经网络（CNN）。 CNN是一种馈送人造神经 网络旨在处理层次的连接网络中的信息，以模仿 哺乳动物视网膜和视觉皮层中的真实神经网络。与大多数CNN成像模型不同 经过培训以对静态图像进行预测，我们已经培训了CNN到相邻框架的输入 预测包括过去和未来的信息，从而有效地在两个领域进行卷积 以及推断粒子位置的时间。现在，类似的图像分析原则也是由 自动驾驶技术的开发商可以区分道路上不同物体的动作。我们 已将我们的CNN跟踪算法应用于各种2D电影，以捕获动态动作 纳米颗粒，病毒和高度运动细菌，几乎无需就可以节省30倍的时间 进行人工干预的同时保持健壮的跟踪性能 负率）。在此STTR提案中，我们试图专注于对神经的进一步优化和测试 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 网络跟踪器启用准确的3D PT（AIM 2）。我们的愿景是将PT推广为研究工具 研究人员通过最大程度地减少与PT分析相关的时间和人工成本。

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.