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

基本信息

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
项目类别：
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

项目摘要

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（PT）是一种强大的生物物理工具，用于阐明分子相互作用、运输复杂生物环境中的现象和流变特性。不幸的是，PT 仍然是一个小众市场生命和物理科学领域的工具，用户群有限，很大程度上是由于大量的时间和技术从录制的电影中提取准确的时变位置数据的限制。这些限制是在低信噪比或显着异质性的实验中加剧，经常遇到生物体液中的纳米颗粒和病原体。目前可用的软件尝试为了使电影分析过程自动化，几乎完全依赖于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，当应用到实际中时实验数据具有很大的空间和时间异质性，当前的软件通常会产生大量误报（即跟踪伪影）或漏报（即丢失实际痕迹），以及经常两者兼而有之。因此，即使在使用时也需要频繁的用户干预来确保准确的跟踪复杂的跟踪软件，显着降低了实验吞吐量并导致大量的用户分析数据的用户差异。精确的粒子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，实验极其昂贵科学。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，尽管获得此类电影的显微镜能力很容易获得可以从 3D PT 中获得重要的科学见解。为了规避现有的挑战粒子跟踪软件，我们开发了一种新的粒子识别和跟踪方法，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网络设计用于在模拟组织的分层连接网络中处理信息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 成像模型不同的是经过训练对静态图像进行预测，我们训练了 CNN 来输入相邻帧，以便每个预测包括来自过去和未来的信息，从而在两个空间中有效地执行卷积以及推断粒子位置的时间。现在正在利用类似的图像分析原理自动驾驶汽车技术的开发人员可以区分道路上不同物体的运动。我们已将我们的 CNN 跟踪算法应用于各种 2D 电影，捕捉物体的动态运动纳米颗粒、病毒和高活力细菌，几乎无需任何操作即可节省至少 30 倍的时间用于人为干预，同时保持强大的跟踪性能（即低误报和低误报）负利率）。在这个 STTR 提案中，我们寻求专注于我们的神经网络的进一步优化和测试用于 2D PT 的网络跟踪平台，包括云计算的使用（目标 1），以及扩展我们的神经网络网络跟踪器以实现精确的 3D PT（目标 2）。我们的愿景是在人群中普及 PT 作为一种研究工具研究人员通过最大限度地减少与 PT 分析相关的时间和劳动力成本。