Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet
Document Type
Article
Publication Date
1-1-2022
Abstract
Multiple sclerosis (MS) is an autoimmune disease that causes mild to severe issues in the central nervous system (CNS). Early detection and treatment are necessary to reduce the harshness of the disease in individuals. The proposed work aims to implement a convolutional neural network (CNN) segmentation scheme to extract the MS lesion in a 2D brain MRI slice. To achieve a better MS detection, this work implemented the VGG-UNet scheme in which the pretrained VGG19 is considered as the encoder section. This scheme is tested on 30 patient images (600 images with dimension 512×512×3 pixels), and the experimental outcome confirms that this scheme provides a better result compared to traditional UNet, SegNet, VGG-UNet, and VGG-SegNet. The experimental investigation implemented on axial, coronal and sagittal plane 2D slices of Flair modality confirms that this work provides a better value of Jaccard (>85%), Dice (>92%), and accuracy (>98%).
Publication Title
Computational Intelligence and Neuroscience
DOI
10.1155/2022/4928096
Recommended Citation
Krishnamoorthy, Sujatha; Zhang, Yaxi; Kadry, Seifedine; and Yu, Weifeng, "Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet" (2022). Kean Publications. 785.
https://digitalcommons.kean.edu/keanpublications/785