FluoRender: Rapid Quantitative Analysis and Adaptive Workflows for Fluorescence Microscopy Data in Fundamental Biomedical Research

总结 FluoRender是一个软件包，用于多通道和多维的交互式可视化和分析 荧光显微镜数据。该项目将满足生物学家利用荧光显微镜的迫切需求， 灵活可靠的数据分析，解决基础生物医学研究中需要快速 测量和工作流原型。具体目标1：互动和协作的衡量和分析 大型多维显微镜数据。我们将增加专为三个试点设计的快速测量工具 我们在犹他州大学的密切合作者的研究。FluoRender将充分利用最新的图形 处理单元（GPU）计算技术和流式处理，以交互速度处理大数据， 合作项目的成功。具体目标2：将机器学习应用于用户工作流程和数据分析。 我们将支持FluoRender用户的各种数据分析需求，并使用 机器学习我们将把用户交互纳入人在回路的方法中，以解决以下问题： 训练示例不足，并增强机器学习的可解释性。具体目标3： FluoRender和其他流行的开源图像分析软件。我们将支持调用ImageJ/Fiji模块， FluoRender用户界面。用户将能够应用熟悉的ImageJ/Fiji功能，并结合FluoRender交互式 工具.频繁访问的外部函数将转换为本地FluoRender实现，以提高 效率和准确性。具体目标4：沉浸式体积数据呈现。我们将支持增强现实 (AR)用于沉浸式数据分析的耳机和全息显示器。这些新兴的显示技术将有更多的 自然的用户交互比虚拟现实（VR）设备更好，并且有利于在科学研究中分析3D数据。 research.

Summary FluoRender is a software package for interactive visualization and analysis of multichannel and multidimensional fluorescence microscopy data. This project will serve the pressing needs of biologists utilizing fluorescence microscopy for flexible and reliable data analysis and address the problems in fundamental biomedical research that demands rapid measurements and workflow prototyping. Specific Aim 1: Interactive and collaborative measurement and analysis of large multidimensional microscopy data. We will add rapid measurement tools specifically designed for three pilot studies of our close collaborators at the University of Utah. FluoRender will take full advantage of latest graphics processing unit (GPU) computing techniques and streamed processing to handle large data at interactive speed, ensuring the success of the collaborative projects. Specific Aim 2: Applying machine learning to user workflows and data analysis. We will support diverse data analysis needs from FluoRender users and provide automatic workflow assembly using machine learning. We will incorporate user interactions in a human-in-the-loop approach to address the problem of insufficient training examples and enhance interpretability in machine learning. Specific Aim 3: Interoperability between FluoRender and other popular open-source image analysis software. We will support invoking ImageJ/Fiji modules from FluoRender user interface. Users will be able to apply familiar ImageJ/Fiji functions combined with FluoRender interactive tools. Frequently accessed external functions will be converted to native FluoRender implementations to improve efficiency and accuracy. Specific Aim 4: Immersive volumetric data presentation. We will support the augmented reality (AR) headsets and holographic displays for immersive data analysis. These emerging display technologies will have more natural user interactions than the virtual reality (VR) devices and be advantageous for analyzing 3D data in scientific research.