# AI-driven biomarker analysis of intact whole brains imaged at micron and sub-micron resolution

> **NIH NIH R43** · LIFECANVAS TECHNOLOGIES, INC. · 2022 · $220,618

## Abstract

Abstract. Whole-organ 3D immunohistochemistry is revolutionizing the field of neuroscience, enabling
unprecedented insight into the distribution of neural cells and neurological markers throughout the brain in health
and disease. LifeCanvas Technologies is at the forefront of the new field of spatial proteomics, providing a
complete workflow for whole-organ preservation, tissue clearing, immunohistochemical labeling, and imaging.
Nevertheless, an ongoing challenge for such studies is the need to rapidly, reproducibly and rigorously quantify
terabyte-sized datasets from whole-organ imaging efforts. While progress has been made in applying Artificial
Intelligence (AI) tools to enable detection of cellular and sub-cellular markers in neural tissue, one-size-fits-all
algorithms are inadequate for analyzing complex, information-rich brain datasets due to varying biomolecular
expression patterns (e.g. nuclear, cytoplasmic, membrane-bound) and region-specific heterogeneities in cell
density and neural cell types. However, AI-driven algorithms targeting a subset of labeling patterns can be
effective provided the availability of adequate training data. LifeCanvas Technologies LCT is optimally positioned
to develop highly accurate algorithms serving a wide range of detection tasks through its access to high volumes
of whole-organ image data containing a variety of label expression patterns via its Contract Research
Organization and user base. LCT proposes to develop a data analysis program, SmartAnalytics, which will
embed a suite of AI algorithms within a user-friendly software package to identify labeled cell locations and
characterize morphological features across the whole brain at cellular and sub-cellular resolution. Specifically,
LCT will use intact, 3D immunolabeled mouse brains to design AI algorithms to detect labeled cells imaged at
cellular resolution and generate further algorithms for the segmentation of labeled features imaged at sub-micron
resolution. Data from LCT’s Contract Research Organization and academic collaborations will be continually fed
back to improve and expand the library of detection algorithms available within SmartAnalytics, and these
developments will drive further customer adoption and enhancement of future versions of the software.
SmartAnalytics will guide users through model application, quality-control testing, and the generation of output
products such as figures and summary statistics. In summary, SmartAnalytics will be an evolving and user-
friendly workflow execution program that enables neuroscientists to take full advantage of their 3D image data,
driving new discoveries in brain function, development and disease.

## Key facts

- **NIH application ID:** 10330017
- **Project number:** 5R43MH125512-02
- **Recipient organization:** LIFECANVAS TECHNOLOGIES, INC.
- **Principal Investigator:** Katherine Cora Ames
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $220,618
- **Award type:** 5
- **Project period:** 2021-02-01 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10330017

## Citation

> US National Institutes of Health, RePORTER application 10330017, AI-driven biomarker analysis of intact whole brains imaged at micron and sub-micron resolution (5R43MH125512-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10330017. Licensed CC0.

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