Automatic detection and localization of Focal Cortical Dysplasia lesions in MRI using fully convolutional neural network. (July 2019)

• Record Type: Journal Article
• Authors: Bijay Dev, K.M.; Jogi, Pawan S.; Niyas, S.; Vinayagamani, S.; Kesavadas, Chandrasekharan; Rajan, Jeny
• Is Part Of: Biomedical signal processing and control. Volume 52(2019)
• Page Start: 218
• Page End: 225
• Publication Date: 2019-07
• Subjects: Convolutional neural networks -- Focal cortical dysplasia -- Magnetic resonance imaging -- Segmentation; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2019.04.024
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A CNN based model for detecting focal cortical dysplasia (FCD) from MRI FLAIR images is proposed. Used a customized version of U-Net architecture. FCD detection rate (recall) of the proposed model is 82.5 (33/40 patients detected correctly). The proposed automated method can be a valuable tool that helps the neuroradiologists in pre-surgical evaluation of patients with intractable epilepsy. Abstract: Focal 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).