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. 2022 Jun 2:2022:4928096.
doi: 10.1155/2022/4928096. eCollection 2022.

Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet

Sujatha Krishnamoorthy  1   2 , Yaxi Zhang  3 , Seifedine Kadry  4 , Weifeng Yu  5
Affiliations

Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet

Sujatha Krishnamoorthy et al. Comput Intell Neurosci. .

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%).

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Structure of the proposed MS lesion segmentation work.
Figure 2
Figure 2
Sample test images and related GT.
Figure 3
Figure 3
Proposed VGG-SegNet scheme to evaluate MS lesion.
Figure 4
Figure 4
An experimental result achieved with the proposed VGG-UNet. (a) Training data. (b) Convergence of VGG-UNet training. (c) Validation outcome.
Figure 5
Figure 5
Sample test images and the segmented MS lesion with VGG-UNet. (a) Sagittal. (b) Axial. (c) Coronal.
Figure 6
Figure 6
Glyph plot demonstrating the overall performance.
Figure 7
Figure 7
Spider plot demonstrating the overall performance of the CNN segmentation schemes on the chosen images. (a) Sagittal plane images. (b) Axial plane images. (c) Coronal plane images.
Figure 8
Figure 8
Comparison of Dice score of VGG-UNet with other schemes.
See this image and copyright information in PMC

References

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