Improved particle swarm optimized deep convolutional neural network with super-pixel clustering for multiple sclerosis lesion segmentation in brain MRI imaging,International Journal for Numerical Methods in Biomedical Engineering

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Improved particle swarm optimized deep convolutional neural network with super-pixel clustering for multiple sclerosis lesion segmentation in brain MRI imaging
International Journal for Numerical Methods in Biomedical Engineering ( IF 2.2 ) Pub Date : 2021-06-28 , DOI: 10.1002/cnm.3506
R Krishna Priya ₁ , Susamma Chacko ₂

Affiliation

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.

中文翻译：

具有超像素聚类的改进粒子群优化深度卷积神经网络用于脑 MRI 成像中的多发性硬化病灶分割

影响脑轴突周围绝缘髓鞘的中枢神经系统 (CNS) 疾病称为多发性硬化症 (MS)。在当今世界，MS 被广泛使用 MRI 诊断和监测，因为结构 MRI 对白质病变在空间和时间方面的传播具有敏感性。本研究的主要目的是使用优化的深度卷积神经网络和超像素聚类在脑 MRI 成像中提出多发性硬化病灶分割。所提出的方法中包含的三个阶段是：（a）预处理，（b）超像素的分割，以及（c）超像素的分类。在第一阶段，通过执行预处理步骤完成图像增强和头骨剥离。在第二阶段，通过在体积的每个切片上应用 SLICO 算法来分割 MS 病变和非 MS 病变区域。在第四阶段，使用这个分割的病灶和非病灶区域进行 CNN 训练和分类。为了处理这个复杂的任务，应用了新开发的基于改进的粒子群优化 (IPSO) 的优化卷积神经网络分类器。在临床 MS 数据上，与其他评估方法相比，该方法在 WM 病变的准确分割方面表现出显着提高。应用了新开发的基于改进的粒子群优化 (IPSO) 的优化卷积神经网络分类器。在临床 MS 数据上，与其他评估方法相比，该方法在 WM 病变的准确分割方面表现出显着提高。应用了新开发的基于改进的粒子群优化 (IPSO) 的优化卷积神经网络分类器。在临床 MS 数据上，与其他评估方法相比，该方法在 WM 病变的准确分割方面表现出显着提高。

更新日期：2021-06-28

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