# Post-surgical resection mapping in epilepsy using convolutional neural networks

> **NIH NIH R21** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2020 · $462,846

## Abstract

Project Summary
Approximately one third of all individuals with epilepsy continue to have seizures despite treatment with anti-
seizure medications. For these people, surgical removal of brain tissue can be a highly effective intervention to
reduce or stop seizures. However, there is considerably variability in post-surgical seizure outcomes among
individual patients, and the ability of physicians to predict who will benefit from surgery is limited. The location
and extent of removed tissue, as well as neuroanatomical structures that are not surgically removed, are
important factors that contribute to post-surgical outcomes. The goal of this proposal is to use convolutional
neural networks, also known as deep learning, to map both the location and extent of surgically removed tissue
on postsurgical MRI scans. The technique will also be used to automatically label brain regions that are spared
during the surgical procedure. These computational tools will allow researchers to develop improved methods to
predict postsurgical health outcomes.
We will develop the automated method by training convolutional neural networks to identify brain regions on MRI
scans obtained after epilepsy surgery at the New York University Langone Medical Center. CNNs have been
specifically designed for the identification of complex spatial patterns in images and are likely to be well-suited
to the identifications of changes in the brain following surgery. Recent developments in computer hardware and
analysis methods mean that CNNs can now be applied to high resolution three-dimensional MRI scans. This
project will leverage these recent developments in computational image analysis to improve our ability to predict
outcomes following epilepsy surgery and therefore contribute to improved treatment for epilepsy patients.

## Key facts

- **NIH application ID:** 10041126
- **Project number:** 1R21NS117990-01
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Heath Pardoe
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $462,846
- **Award type:** 1
- **Project period:** 2020-06-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10041126

## Citation

> US National Institutes of Health, RePORTER application 10041126, Post-surgical resection mapping in epilepsy using convolutional neural networks (1R21NS117990-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10041126. Licensed CC0.

---

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