Remote Instrumentation Science Environment for Intelligent Image Analytics
Remote Instrumentation Science Environment for Intelligent Image Analytics
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DOI：
10.1109/escience55777.2022.00023
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Mauro Lemus Alarcon;Songjie Wang;N. P. Nguyen;Ashish Pandey;F. Bunyak;Matthew R. Maschmann;K. Palaniappan;P. Calyam
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文献类型：
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作者：
Mauro Lemus Alarcon;Songjie Wang;N. P. Nguyen;Ashish Pandey;F. Bunyak;Matthew R. Maschmann;K. Palaniappan;P. Calyam
当前的科学实验经常涉及对专用仪器的控制（例如，扫描电子显微镜），从这些仪器收集图像数据，并将数据传输到模拟中心进行处理。这个过程需要一个“人在回路”来手动执行这些任务，除了需要大量的精力和时间外，还可能导致不一致或错误。因此，必须有一个能够执行远程仪器的自动化系统，以智能地控制和收集来自科学仪器的数据。在本文中，我们提出了一种用于智能图像分析的远程仪器科学环境（RISE），该环境提供了安全捕获图像的基础设施，通过机器学习确定过程参数，并通过自动化提供实验控制操作，前提是“人在回路上”。RISE中的机器学习有助于迭代发现过程，以帮助研究人员调整仪器设置，从而改善实验结果。在图像分析管道的两个科学用例的驱动下，一个在材料科学中，另一个在生物医学科学中，我们展示了RISE自动化如何利用云计算，内部HPC集群和显微镜上可用的Python编程接口的尖端集成。使用Web服务，我们实现RISE执行自动化的图像数据收集/分析的指导下，智能代理提供实时反馈控制的显微镜使用图像分析输出。我们的评估结果显示了RISE的优势，研究人员可以获得更高的图像分析精度，节省手动控制显微镜的宝贵时间，同时减少操作仪器的错误。
Current scientific experiments frequently involve control of specialized instruments (e.g., scanning electron microscopes), image data collection from those instruments, and transfer of the data for processing at simulation centers. This process requires a “human-in-the-loop” to perform those tasks manually, which besides requiring a lot of effort and time, could lead to inconsistencies or errors. Thus, it is essential to have an automated system capable of performing remote instrumentation to intelligently control and collect data from the scientific instruments. In this paper, we propose a Remote Instrumentation Science Environment (RISE) for intelligent image analytics that provides the infrastructure to securely capture images, determine process parameters via machine learning, and provide experimental control actions via automation, under the premise of “human-on-the-loop”. The machine learning in RISE aids an iterative discovery process to assist researchers to tune instrument settings to improve the outcomes of experiments. Driven by two scientific use cases of image analytics pipelines, one in material science, and another in biomedical science, we show how RISE automation leverages a cutting-edge integration of cloud computing, on-premise HPC cluster, and a Python programming interface available on a microscope. Using web services, we implement RISE to perform automated image data collection/analysis guided by an intelligent agent to provide real-time feedback control of the microscope using the image analytics outputs. Our evaluation results show the benefits of RISE for researchers to obtain higher image analytics accuracy, save precious time in manually controlling the microscopes, while reducing errors in operating the instruments.