Segmentation of focal cortical dysplasia lesions from magnetic resonance images using 3D convolutional neural networks. (September 2021)

• Record Type: Journal Article
• Authors: Niyas, S.; Chethana Vaisali, S.; Show, Iwrin; Chandrika, T.G.; Vinayagamani, S.; Kesavadas, Chandrasekharan; Rajan, Jeny
• Is Part Of: Biomedical signal processing and control. Volume 70(2021)
• Publication Date: 2021-09
• Subjects: Focal cortical dysplasia -- Deep learning -- Convolutional neural networks -- Image segmentation -- Magnetic resonance imaging; 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.2021.102951
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Highlights: Focal Cortical Dysplasia (FCD) is the most common cause of refractory epilepsy. A 3D Res-UNet model is proposed for the segmentation of FCD from MRI volumes. A shallow sliced stacking approach is used to reduce the size of input 3D samples. The proposed model holds the advantages of both 2D and 3D CNN architectures. Abstract: Computer-aided diagnosis using advanced Artific ial Intelligence (AI) techniques has become much popular over the last few years. This work automates the segmentation of Focal Cortical Dysplasia (FCD) lesions from three-dimensional (3D) Magnetic Resonance (MR) images. FCD is a type of neuronal malformation in the brain cortex and is the leading cause of intractable epilepsy, irrespective of gender or age differences. Since the neuron related abnormalities are usually resistant to drug therapy, surgical resection has been the main treatment approach for patients with intractable epilepsy. Automating the identification and segmentation of FCD is useful for neuroradiologists in pre-surgical evaluations. Convolutional Neural Networks (CNNs) have the ability to learn appropriate features from the training data without any human intervention. But, most of the state-of-the-art FCD segmentation approaches use two-dimensional (2D) CNN models despite the availability of 3D Magnetic resonance imaging (MRI) volumes, and hence fail to leverage the inter-slice information present in the MRI volumes. The major hurdles in considering a 3D CNN model are the need for a large 3D dataset, big memory, and high computation cost. A deep 3D CNN segmentation model, which can extract inter-slice information and overcomes the drawbacks of conventional 3D CNN methods to an extent, is proposed in this paper. The model uses a 3D version of U-Net with residual blocks that works on shallow depth 3D sub-volumes generated from MRI volumes. The proposed method shows superior performance over the state-of-the-art FCD segmentation methods in both qualitative and quantitative analysis.