# Neuroinformatics platform using machine learning and content-based image retrieval for neuroscience image data

> **NIH NIH R44** · MICROBRIGHTFIELD, LLC · 2020 · $749,716

## Abstract

This project aims to develop NeuroManager™, an innovative neuroinformatics platform for advanced parsing,
storing, aggregating, analyzing and sharing of complex neuroscience image data. A core technology that we will
develop in NeuroManager will be Image Content Analysis for Retrieval Using Semantics (ICARUS), a novel,
intelligent neuroimage curation system that will enable image retrieval based on visual appearance or by
semantic concept. ICARUS will use machine learning applied to content-based image retrieval - (CBIR) to build
and refine models that summarize microscopic and macroscopic image appearance and automatically assign
semantic concepts to neuroimages. Neuroscience research generates extensive, multifaceted data that is
considerably under-utilized because access to original raw data is typically maintained by the source lab. On the
other hand, there are many advantages in sharing complex image data in neuroscience research, including the
opportunity for separate analysis of raw data by other scientists from another perspective and improved
reproducibility of scientific studies and their results. Unfortunately, none of the neuroscience data sharing options
that exist today fulfill all the needs of neuroscientists. To solve this problem, NeuroManager will include the
following distinct, significant innovations: (i) versatility for handling two-dimensional (2D) and three-dimensional
neuroimaging data sets from animal models and humans; (ii) functionality to share complex datasets that extends
secure, privacy-controlled paradigms from institutional, laboratory-based and even public domains; (iii) flexibility
to implement NeuroManager within an institute’s IT infrastructure, or on most cloud-based virtualized
environments including Azure, Google Cloud Services and Amazon Web Services; (iv) and most importantly,
the ICARUS technology for CBIR in neuroimaging data sets. The benefit of NeuroManager for the neuroscience
research community, pharmacological and biotechnological R&D, and society in general will be to foster
collaboration between scientists and institutions, promoting innovation through combined expertise in an
interdisciplinary atmosphere. This will open new horizons for better understanding the neuropathology
associated with several human neuropsychiatric and neurological conditions at various levels (i.e.,
macroscopically, microscopically, subcellularly and functionally), ultimately leading to an improved basis for
developing novel treatment and prevention strategies for complex brain diseases. In Phase I we will prove
feasibility of this novel technology by developing prototype software that will perform CBIR on 2D whole slide
images of coronal sections of entire mouse brains from ongoing research projects of our collaborators. Work in
Phase II will focus on developing the commercial software product that will include all of the innovations
mentioned above. A competing technology with comparable functionality, addressing the f...

## Key facts

- **NIH application ID:** 9989186
- **Project number:** 5R44MH118815-03
- **Recipient organization:** MICROBRIGHTFIELD, LLC
- **Principal Investigator:** Paul Angstman
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $749,716
- **Award type:** 5
- **Project period:** 2018-09-17 → 2022-09-16

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9989186

## Citation

> US National Institutes of Health, RePORTER application 9989186, Neuroinformatics platform using machine learning and content-based image retrieval for neuroscience image data (5R44MH118815-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9989186. Licensed CC0.

---

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