Lesion Localization in Paediatric Epilepsy Using Patch-Based Convolutional Neural Network

Abstract

Focal Cortical Dysplasia (FCD) is one of the most common causes of paediatric medically intractable focal epilepsy. In cases of medically resistant epilepsy, surgery is the best option to achieve a seizure-free condition. Pre-surgery lesion localization affects the surgery outcome. Lesion localization is done through examining the MRI for FCD features, but the MRI features of FCD can be subtle and may not be detected by visual inspection. Patients with epilepsy who have normal MRI are considered to have MRI-negative epilepsy. Recent advances in machine learning and deep learning hold the potential to improve the detection and localization of FCD without the need to conduct extensive pre-processing and FCD feature extraction. In this research, we apply Convolutional Neural Networks (CNNs) to classify FCD in children with focal epilepsy and localize the lesion. Two networks are presented here, the first network is applied on the whole-slice of the MR images, and the second network is taking smaller patches extracted from the slices of each MRI as input. The patch-wise model successfully classifies all healthy patients (13 out of 13), while 12 out of 13 cases are correctly identified by the whole-slice model. Using the patch-wise model, we identified the lesion in 17 out of 17 MR-positive subjects with coverage of 85% and for MR-negative subjects, we identify 11 out of 13 FCD subjects with lesion coverage of 66%. The findings indicate that convolutional neural network is a promising tool to objectively identify subtle lesions such as FCD in children with focal epilepsy.