# FluoRender: Visualization-Based and Interactive Analysis for  Multichannel Microscopy Data

> **NIH NIH R01** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2020 · $343,125

## Abstract

Summary
 FluoRender is a software package for visualizing and analyzing 3D and 4D (3D over time)
fluorescence microscopy data. This project will serve the needs of biologists utilizing confocal
microscopy for understanding cell development in many organisms and addresses the big-data
problem from the massive increase of imaging data from modern high-resolution fluorescence
microscopes. Specific Aim 1: Visualization of an extended number of volume channels:
FluoRender will be enhanced with the multichannel visualization capability by simultaneously
supporting several tens to hundreds of channels, which can be acquired from multispectral
imaging devices or by registering data of multiple scans. FluoRender will take advantage of the
latest volume rendering techniques to visualize significantly improved signal intensity detail
compared to pseudo-surfaces. Specific Aim 2: Interactive comparison and organization of
volume channels: A package of measures will be implemented in FluoRender for directly
comparing volume channels. Leveraging the OpenCL programming interface, shape comparisons
will be performed interactively on graphics hardware, allowing compound measures for complex
morphology as well as immediate visual feedback via multichannel visualization. Interactive
comparison will further enable the development of functions for semiautomatic channel
organization and multichannel colocalization analysis. Specific Aim 3: 4D tracking of structures
with irregular and changing shapes: Tracking irregularly shaped and shape-changing
structures will substantially expand FluoRender's application for developmental and
morphological studies of intracellular organelles, cells, and tissues. This will include a
comprehensive tracking system that integrates different modules and allows them to work in an
iterative and integrated environment, allowing user-guided, progressive refinement of the
segmentation and tracking results. Specific Aim 4. Fully hardware-accelerated and
customizable computing modules: FluoRender will be restructures using compute modules
based on the OpenCL standard, which provides not only hardware-accelerated execution speed,
but also convenience for customization and reuse. Computing modules will be integrated with
visualization features, enabling interactive and visualization-centered analysis. Users will also be
able to reorganize and build modules to customize specific workflows for great adaptability.

## Key facts

- **NIH application ID:** 9916753
- **Project number:** 5R01EB023947-04
- **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:** 2020
- **Award amount:** $343,125
- **Award type:** 5
- **Project period:** 2017-08-01 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9916753

## Citation

> US National Institutes of Health, RePORTER application 9916753, FluoRender: Visualization-Based and Interactive Analysis for  Multichannel Microscopy Data (5R01EB023947-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9916753. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*