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

> **NIH NIH R01** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2021 · $1,143,750

## Abstract

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.

## Key facts

- **NIH application ID:** 10276704
- **Project number:** 1R01EB031872-01
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Charles Hansen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,143,750
- **Award type:** 1
- **Project period:** 2021-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10276704

## Citation

> US National Institutes of Health, RePORTER application 10276704, FluoRender: Rapid Quantitative Analysis and Adaptive Workflows for Fluorescence Microscopy Data in Fundamental Biomedical Research (1R01EB031872-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10276704. Licensed CC0.

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