Improved particle swarm optimized deep convolutional neural network with super-pixel clustering for multiple sclerosis lesion segmentation in brain MRI imaging
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Updated: Oct 31, 2025
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Published on: December 15, 2023
R Krishna Priya1, Susamma Chacko2
1Department of Electrical and Communication Engineering, National University of Science and Technology, Oman.
Abstract:
A central nervous system (CNS) disease affecting the insulating myelin sheaths around the brain axons is called multiple sclerosis (MS). In today's world, MS is extensively diagnosed and monitored using the MRI, because of the structural MRI sensitivity in dissemination of white matter lesions with respect to space and time. The main aim of this study is to propose Multiple Sclerosis Lesion Segmentation in Brain MRI imaging using Optimized Deep Convolutional Neural Network and Super-pixel Clustering. Three stages included in the proposed methodology are: (a) preprocessing, (b) segmentation of super-pixel, and (c) classification of super-pixel. In the first stage, image enhancement and skull stripping is done through performing a preprocessing step. In the second stage, the MS lesion and Non-MS lesion regions are segmented through applying SLICO algorithm over each slice of the volume. In the fourth stage, a CNN training and classification is performed using this segmented lesion and non-lesion regions. To handle this complex task, a newly developed Improved Particle Swarm Optimization (IPSO) based optimized convolutional neural network classifier is applied. On clinical MS data, the approach exhibits a significant increase in the accuracy segmenting of WM lesions when compared with the rest of evaluated methods.
Insights
Multiple sclerosis (MS) is a CNS disease impacting brain myelin. This study introduces an optimized deep convolutional neural network with super-pixel clustering for accurate MS lesion segmentation in brain MRI scans.
Area of Science:
- Neurology
- Medical Imaging
- Artificial Intelligence
Background:
- Multiple sclerosis (MS) is a central nervous system (CNS) disease affecting myelin sheaths.
- Magnetic Resonance Imaging (MRI) is crucial for diagnosing and monitoring MS due to its sensitivity to white matter lesions.
Purpose of the Study:
- To propose an optimized deep convolutional neural network (CNN) and super-pixel clustering method for segmenting Multiple Sclerosis (MS) lesions in brain MRI.
- To enhance the accuracy and efficiency of MS lesion detection in clinical practice.
Main Methods:
- The proposed methodology involves three stages: preprocessing (image enhancement, skull stripping), super-pixel segmentation using the SLICO algorithm, and classification.
- An Improved Particle Swarm Optimization (IPSO) algorithm is utilized to optimize a deep convolutional neural network (CNN) for classifying segmented regions.
Main Results:
- The optimized CNN approach demonstrated a significant increase in the accuracy of segmenting white matter (WM) lesions.
- The method outperformed existing techniques in segmenting MS lesions on clinical MRI data.
Conclusions:
- The developed method provides an effective and accurate approach for Multiple Sclerosis lesion segmentation in brain MRI.
- This technique holds promise for improving the diagnosis and monitoring of MS patients.
