Zhejiang Bioinformatics International Science and Technology Cooperation Center, Wenzhou-Kean University, Wenzhou, Zhejiang Province, China.
Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics, Wenzhou-Kean University, Wenzhou, Zhejiang Province, China.
Comput Intell Neurosci. 2022 Jun 2;2022:4928096. doi: 10.1155/2022/4928096. eCollection 2022.
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%).
多发性硬化症（MS）是一种自身免疫性疾病，可导致中枢神经系统（CNS）出现轻度至重度问题。早期发现和治疗对于减轻个体疾病的严重程度非常必要。本项工作旨在实施卷积神经网络（CNN）分割方案，以提取 2D 脑 MRI 切片中的 MS 病变。为了更好地检测 MS，本工作实施了 VGG-UNet 方案，其中预训练的 VGG19 被视为编码器部分。该方案在 30 个患者图像（维度为 512×512×3 像素的 600 张图像）上进行了测试，实验结果证实与传统 UNet、SegNet、VGG-UNet 和 VGG-SegNet 相比，该方案提供了更好的结果。在 Flair 模式的轴位、冠状位和矢状位 2D 切片上进行的实验研究证实，本工作提供了更好的 Jaccard 值（>85%）、Dice 值（>92%）和准确性（>98%）。
