Bijay Dev, K.M.Pawan, P.S.Niyas, S.Vinayagamani, S.Kesavadas, C.Rajan, J.2026-02-052019Biomedical Signal Processing and Control, 2019, 52, , pp. 218-22517468094https://doi.org/10.1016/j.bspc.2019.04.024https://idr.nitk.ac.in/handle/123456789/24487Focal cortical dysplasia (FCD) is the leading cause of drug-resistant epilepsy in both children and adults. At present, the only therapeutic approach in patients with drug-resistant epilepsy is surgery. Hence, the quantification of FCD via non-invasive imaging techniques helps physicians to decide on surgical interventions. The properties like non-invasiveness and capability to produce high-resolution images makes magnetic resonance imaging an ideal tool for detecting the FCD to an extent. The FCD lesions vary in size, shape, and location for different patients and make the manual detection time consuming and sensitive to the experience of the observer. Automatic segmentation of FCD lesions is challenging due to the difference in signal strength in images acquired with different machines, noise, and other kinds of distortions such as motion artifacts. Most of the methods proposed in the literature use conventional machine learning and image processing techniques in which their accuracy relies on the trained features. Hence, feature extraction should be done more precisely which requires human expertise. The ability to learn the appropriate features/representations from the training data without any human interventions makes the convolutional neural network (CNN) the suitable method for addressing these drawbacks. As far as we are aware, this work is the first one to use a CNN based model to solve the aforementioned problem using only MRI FLAIR images. We customized the popular U-Net architecture and trained the proposed model from scratch (using MRI images acquired with 1.5T and 3T scanners). FCD detection rate (recall) of the proposed model is 82.5 (33/40 patients detected correctly). © 2019ConvolutionImage acquisitionImage segmentationMagnetic resonance imagingNeurologySurgeryAutomatic DetectionAutomatic segmentationsConventional machinesFocal cortical dysplasiasHigh resolution imageImage processing techniqueNon-invasive imagingSurgical interventionsConvolutional neural networksArticleartifactautomationclinical articleconvolutional neural networkcortical dysplasiafeature extractionhumanimage processingimage segmentationintractable epilepsymachine learningnerve cell networkneuroimagingnuclear magnetic resonance imagingpriority journalretrospective study