骨关节炎
射线照相术
医学
卷积神经网络
接头(建筑物)
纹理(宇宙学)
膝关节
关节病
口腔正畸科
人工智能
计算机科学
放射科
外科
病理
图像(数学)
建筑工程
替代医学
工程类
作者
Neslihan Bayramoglu,Miika T. Nieminen,Simo Saarakkala
出处
期刊:Cornell University - arXiv
日期:2020-05-24
摘要
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%
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