Deep Learning-Based Lesion Segmentation in Paediatric Epilepsy

In this research, our goal is to implement a Convolutional Neural Network (CNN) to segment Focal Cortical Dysplasia (FCD). FCD is a common lesion responsible for paediatric medically intractable focal epilepsy. MRI features of FCD can be subtle and may be missed by a radiologist. Recent advances in deep learning techniques in different fields have motivated us to develop a deep learning-based model to detect and segment the lesion responsible for FCD. We proposed a Fully Convolutional Network (FCN) for the task of FCD detection and localization. Our proposed model has four blocks of two convolutional layers followed by a pooling layer, as feature extraction part. Then, we have added three up-sampling blocks which include one convolutional layer and one up-sampling layer. The convolutional layers' kernels are 3 x 3 and we are utilizing 4x and 2x upsampling layers in the decoder part. We are using skip layers as well, to get a more refined up-sampled output. We are adding the respective down-sampled feature map from the encoder part. To train and evaluate the model leaveone-out technique has been utilized where one test subject is left out of training in each experiment. We have identified 13 out 13 healthy subjects as healthy. The model has identified the lesion in 15 out of 17 MR-positive FCD subjects with 73 percent lesion coverage. For MR-negative cases, 11 out of 13 subjects were identified with lesion coverage of 64 percent. Based on our experiments, FCN holds the potential to assist specialists in detecting and localizing the lesion.