论文标题
使用膝关节磁共振图像的深度学习对膝关节骨关节炎的早期检测
Early detection of knee osteoarthritis using deep learning on knee magnetic resonance images
论文作者
论文摘要
这项研究的目的是研究MRI和患者数据对使用不同深度学习体系结构预测膝关节骨关节炎（OA）发生率的影响。使用来自骨关节炎倡议的593例患者的中等加权涡轮自旋回波（IW-TSE）序列预测24个月内的膝关节OA发病率。为了从IW-TSE序列中提取包含膝关节的感兴趣区域，对U-NET模型进行了训练，并用于在双回声稳态（DESS）序列上分割骨头。随后，注册了IW-TSE和DESS序列，并将DESS分割转换为相应的IW-TSE扫描。使用三种不同的深度学习结构测试了基于MRI的特征在膝关节OA发病率预测中的性能：残留网络（RESNET），密度连接的卷积网络（Densenet）和卷积变量自动码编码器（CVAE）。为了评估单独基于MRI的特征的预测性能，将ResNet，Densenet和CVAE的输出与患者数据（即年龄，性别，BMI）结合在一起，并用作逻辑回归（LR）分类器的输入。根据视觉MRI和基于X射线的OA功能定义了膝关节OA。 Resnet和Densenet显示出相似的结果，两种方法都在接收器操作特征曲线（AUC）值下面具有高达0.6269的值。当与患者数据结合使用时，最佳性能OA检测模型是CVAE，AUC为0.6699，而单独用作LR分类器的输入时，AUC为0.6689。结果表明，使用IW-TSE MRI时，三种深度学习算法具有相似的指标，并且随着患者数据的包含，它们的性能提高，这表明了变量（例如年龄，性别和BMI）对膝关节OA检测的强烈影响。
The aim of this study was to investigate the influence of MRI and patient data on the prediction of knee osteoarthritis (OA) incidence using different deep learning architectures. Knee OA incidence within 24 months was predicted using the intermediate-weighted turbo spin-echo (IW-TSE) sequence of 593 patients from the Osteoarthritis Initiative. To extract a region of interest containing the knee joint from the IW-TSE sequence, a U-Net model was trained and used to segment bone on a dual echo steady state (DESS) sequence. Subsequently, IW-TSE and DESS sequences were registered and the DESS segmentations were transformed to the corresponding IW-TSE scans. The performance of MRI-based features in the prediction of knee OA incidence was tested using three different deep learning architectures: a residual network (ResNet), a densely connected convolutional network (DenseNet), and a convolutional variational autoencoder (CVAE). To evaluate the predictive performance of MRI-based features alone, the outputs of ResNet, DenseNet, and CVAE were coupled with patient data (i.e., age, gender, BMI) and used as input to a Logistic Regression (LR) Classifier. Knee OA was defined based on visual MRI and X-ray-based OA features. The ResNet and DenseNet showed similar results, with both methods having the area under the receiver operating characteristic curve (AUC) values up to 0.6269. The best performing OA detection model was CVAE with an AUC of 0.6699 when combined with patient data and an AUC of 0.6689 when used alone as input to the LR classifier. The results showed that three deep learning algorithms have similar metrics when using IW-TSE MRIs and their performance increased with the inclusion of patient data, which shows the strong influence of variables such as age, gender, and BMI on the detection of knee OA.
