Post-surgical resection mapping in epilepsy using convolutional neural networks

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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.