论文标题
严重性分类与胶原蛋白VI相关的肌病具有卷积神经网络和手工纹理功能
Severity classification in cases of Collagen VI-related myopathy with Convolutional Neural Networks and handcrafted texture features
论文作者
论文摘要
磁共振成像（MRI）是用于低渗透神经肌肉疾病临床评估的非侵入性工具。自动诊断方法可能会减少对活检的需求，并提供有关疾病随访的宝贵信息。在本文中，提出了三种方法，以根据其参与程度（尤其是卷积神经网络，完全连接的网络来对纹理特征分类，以及将两个特征集的混合方法分类的胶原蛋白相关肌病病例中的目标肌肉分类。对26名受试者的轴向T1加权涡轮自旋Echo MRI进行了评估，其中包括乌拉里奇先天性肌肉营养不良症和伯特勒姆肌病患者在不同的进化阶段。对于健康，轻度和中度/严重的病例，混合模型的全球精度分别为93.8％，其全球精度分别为0.99、0.82和0.95，实现了最佳的交叉验证结果。
Magnetic Resonance Imaging (MRI) is a non-invasive tool for the clinical assessment of low-prevalence neuromuscular disorders. Automated diagnosis methods might reduce the need for biopsies and provide valuable information on disease follow-up. In this paper, three methods are proposed to classify target muscles in Collagen VI-related myopathy cases, based on their degree of involvement, notably a Convolutional Neural Network, a Fully Connected Network to classify texture features, and a hybrid method combining the two feature sets. The proposed methods were evaluated on axial T1-weighted Turbo Spin-Echo MRI from 26 subjects, including Ullrich Congenital Muscular Dystrophy and Bethlem Myopathy patients at different evolution stages. The hybrid model achieved the best cross-validation results, with a global accuracy of 93.8%, and F-scores of 0.99, 0.82, and 0.95, for healthy, mild and moderate/severe cases, respectively.
