# Nobrainer: A robust and validated neural network tool suite for imagers

> **NIH NIH RF1** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2020 · $2,419,908

## Abstract

There is an increasing need for efficient and robust software to process, integrate, and offer insight across the
diversity of population imaging efforts underway across the BRAIN Initiative and other projects. Advances in
statistical learning offer a set of technologies that can address many research applications using the extensive
and varied data being produced by the projects. This can transform how we analyze and integrate new data. We
propose using Nobrainer, an open source Python library that leverages these new learning technologies, as a
platform that greatly simplifies integrating deep learning into neuroimaging research. Using this library, we are
building and distributing user-friendly and cloud enabled end-user applications for the neuroimaging
community. In Aim 1, we provide neural network models. We will create robust, pre-trained neural networks
for brain segmentation and time series processing using brain scans from over 65000 individuals. Once
trained, these models can then be used as the basis for many other applications, especially in reducing time of
processing. We will subsequently use these base networks to perform image processing, image correction, and
quality control. In Aim 2, we address the ability to train on private datasets. We will use Bayesian neural
network models, which support principled use of prior information. We will use these networks to help detect
when the models are expected to fail on an input, and provide visualizations to better understand how the
model is working. In Aim 3, we focus on the engineering needed to maintain the software infrastructure,
improve efficiency, and increase the scalability of our training methods. Here, we will extend, maintain, and
disseminate Nobrainer, our open source software framework, together with training materials and ready to use,
cloud-friendly, applications. We will also create much faster, neural network equivalents of time consuming
image processing tasks (e.g., registration, segmentation, and annotation). The Nobrainer tools developed
through these aims will allow users to find and apply the most pertinent applications and developers to extend
the framework to support new architectures and disseminate new models and applications. We expect these
tools to be used by any neuroimaging researcher through integration with BRAIN archives and popular
software packages. These tools will significantly reduce data processing and new model development time, thus
allowing faster exploration of hypotheses using public data and increase reusability of data through greater
trust in model outputs.

## Key facts

- **NIH application ID:** 10021957
- **Project number:** 1RF1MH121885-01A1
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Satrajit Sujit Ghosh
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $2,419,908
- **Award type:** 1
- **Project period:** 2020-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10021957

## Citation

> US National Institutes of Health, RePORTER application 10021957, Nobrainer: A robust and validated neural network tool suite for imagers (1RF1MH121885-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10021957. Licensed CC0.

---

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