基于机器学习的髌骨X线片纹理分析诊断髌股骨关节炎
Machine Learning Based Texture Analysis of Patella from X-Rays for Detecting Patellofemoral Osteoarthritis
摘要：
目的评价膝关节侧位片纹理特征对髌股骨关节炎（PFOA）的诊断价值。我们使用了来自MOST公共使用数据集（n = 5507个膝关节）的膝关节侧位X线片。髌骨感兴趣区（ROI）使用界标检测工具（BoneDrive）自动检测。手工制作的功能，基于本地二进制模式（LBP），然后提取描述髌骨纹理。首先，训练机器学习模型（梯度提升机）以从LBP特征检测放射照相PFOA。此外，我们直接在纹理补丁上使用端到端训练的深度卷积神经网络（CNN）来检测PFOA。最终将提出的分类模型与使用临床评估和参与者特征（如年龄、性别、体重指数（BMI）、总WOMAC评分和胫股Kelling-Lawrence（KL）分级）的更传统的参考模型进行比较。MOST公共使用数据集中提供的专家读者对PFOA状态的视觉评估被用作模型的分类结果。预测模型的性能通过受试者工作特征曲线下面积（ROC AUC）、精确-回忆（PR）曲线下面积-平均精确度（AP）和Brier评分进行评估，采用分层5折交叉验证设置。预测PFOA的最强参考模型（包括年龄、性别、BMI、WOMAC评分和胫股KL分级）的AUC和AP分别为0.817和0.487。使用CNN的纹理ROI分类显著提高了预测性能（ROC AUC= 0.889，AP= 0.714）。我们提出的第一项研究，分析髌骨纹理诊断PFOA。我们的研究结果表明，利用髌骨的纹理特征来预测PFOA的潜力。
英文摘要：
Objective is to assess the ability of texture features for detecting radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs. We used lateral view knee radiographs from MOST public use datasets (n = 5507 knees). Patellar region-of-interest (ROI) was automatically detected using landmark detection tool (BoneFinder). Hand-crafted features, based on LocalBinary Patterns (LBP), were then extracted to describe the patellar texture. First, a machine learning model (Gradient Boosting Machine) was trained to detect radiographic PFOA from the LBP features. Furthermore, we used end-to-end trained deep convolutional neural networks (CNNs) directly on the texture patches for detecting the PFOA. The proposed classification models were eventually compared with more conventional reference models that use clinical assessments and participant characteristics such as age, sex, body mass index(BMI), the total WOMAC score, and tibiofemoral Kellgren-Lawrence (KL) grade. Atlas-guided visual assessment of PFOA status by expert readers provided in the MOST public use datasets was used as a classification outcome for the models. Performance of prediction models was assessed using the area under the receiver operating characteristic curve (ROC AUC), the area under the precision-recall (PR) curve-average precision (AP)-, and Brier score in the stratified 5-fold cross validation setting.Of the 5507 knees, 953 (17.3%) had PFOA. AUC and AP for the strongest reference model including age, sex, BMI, WOMAC score, and tibiofemoral KL grade to predict PFOA were 0.817 and 0.487, respectively. Textural ROI classification using CNN significantly improved the prediction performance (ROC AUC= 0.889, AP= 0.714). We present the first study that analyses patellar bone texture for diagnosing PFOA. Our results demonstrates the potential of using texture features of patella to predict PFOA.
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