Convolutional neural networks for automatic detection of Focal Cortical Dysplasia

TL;DR

This paper develops a deep learning model to automatically detect focal cortical dysplasia in MRI scans, aiming to assist radiologists and improve detection accuracy in epilepsy diagnosis.

Contribution

It introduces an improved deep learning approach for FCD detection and applies it to a new dataset, achieving successful identification in most cases.

Findings

Detected FCD in 11 out of 15 patients

Improved detection accuracy over previous methods

Demonstrated feasibility of automated FCD detection

Abstract

Focal cortical dysplasia (FCD) is one of the most common epileptogenic lesions associated with cortical development malformations. However, the accurate detection of the FCD relies on the radiologist professionalism, and in many cases, the lesion could be missed. In this work, we solve the problem of automatic identification of FCD on magnetic resonance images (MRI). For this task, we improve recent methods of Deep Learning-based FCD detection and apply it for a dataset of 15 labeled FCD patients. The model results in the successful detection of FCD on 11 out of 15 subjects.