论文标题
轻巧的CNN和关节形状连接空间（JS2）放射性骨关节炎检测的描述符
A Lightweight CNN and Joint Shape-Joint Space (JS2) Descriptor for Radiological Osteoarthritis Detection
论文作者
论文摘要
膝关节骨关节炎（OA）是全世界非常普遍的进步和退化性肌肉骨骼疾病，这给由于经济影响而导致生活质量以及社会的患者造成了沉重的负担。因此，任何减轻疾病负担的尝试都可以帮助患者和社会。在这项研究中，我们提出了一种完全自动化的新方法，基于关节形状和卷积神经网络（CNN）的骨纹理特征的组合，以区分患有和没有X光照相骨关节炎的膝盖X光片。此外，我们报告了使用CNN描述骨纹理的首次尝试。实验中使用了骨关节炎倡议（OAI）和多中心骨关节炎（大多数）的膝关节X光片。我们的型号接受了OAI的8953膝盖X光片的培训，并对大多数膝关节X光片进行了评估。我们的结果表明，融合所提出的形状和纹理参数可在ROC曲线（AUC）下达到射线照相OA检测屈服面积的最新性能，为95.21％
Knee osteoarthritis (OA) is very common progressive and degenerative musculoskeletal disease worldwide creates a heavy burden on patients with reduced quality of life and also on society due to financial impact. Therefore, any attempt to reduce the burden of the disease could help both patients and society. In this study, we propose a fully automated novel method, based on combination of joint shape and convolutional neural network (CNN) based bone texture features, to distinguish between the knee radiographs with and without radiographic osteoarthritis. Moreover, we report the first attempt at describing the bone texture using CNN. Knee radiographs from Osteoarthritis Initiative (OAI) and Multicenter Osteoarthritis (MOST) studies were used in the experiments. Our models were trained on 8953 knee radiographs from OAI and evaluated on 3445 knee radiographs from MOST. Our results demonstrate that fusing the proposed shape and texture parameters achieves the state-of-the art performance in radiographic OA detection yielding area under the ROC curve (AUC) of 95.21%
