Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Finland.
Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Finland; Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland.
Osteoarthritis Cartilage. 2020 Jul;28(7):941-952. doi: 10.1016/j.joca.2020.03.006. Epub 2020 Mar 20.
The purposes of this study were to investigate: 1) the effect of placement of region-of-interest (ROI) for texture analysis of subchondral bone in knee radiographs, and 2) the ability of several texture descriptors to distinguish between the knees with and without radiographic osteoarthritis (OA).
Bilateral posterior-anterior knee radiographs were analyzed from the baseline of Osteoarthritis Initiative (OAI) (9012 knee radiographs) and Multicenter Osteoarthritis Study (MOST) (3,644 knee radiographs) datasets. A fully automatic method to locate the most informative region from subchondral bone using adaptive segmentation was developed. Subsequently, we built logistic regression models to identify and compare the performances of several texture descriptors and each ROI placement method using 5-fold cross validation. Importantly, we also investigated the generalizability of our approach by training the models on OAI and testing them on MOST dataset. We used area under the receiver operating characteristic curve (ROC AUC) and average precision (AP) obtained from the precision-recall (PR) curve to compare the results.
We found that the adaptive ROI improves the classification performance (OA vs non-OA) over the commonly-used standard ROI (up to 9% percent increase in AUC). We also observed that, from all texture parameters, Local Binary Pattern (LBP) yielded the best performance in all settings with the best AUC of 0.840 [0.825, 0.852] and associated AP of 0.804 [0.786, 0.820].
Compared to the current state-of-the-art approaches, our results suggest that the proposed adaptive ROI approach in texture analysis of subchondral bone can increase the diagnostic performance for detecting the presence of radiographic OA.
本研究旨在探讨：1）在膝关节 X 线片中对骨皮质下骨进行感兴趣区（ROI）定位对纹理分析的影响，以及 2）几种纹理描述符区分有无放射学骨关节炎（OA）膝关节的能力。
对 Osteoarthritis Initiative（OAI）（9012 张膝关节 X 线片）和 Multicenter Osteoarthritis Study（MOST）（3644 张膝关节 X 线片）数据集的基线双侧后前位膝关节 X 线片进行了分析。开发了一种完全自动的方法，使用自适应分割来定位最具信息量的骨皮质下区域。随后，我们构建了逻辑回归模型，使用 5 折交叉验证来识别和比较几种纹理描述符和每个 ROI 定位方法的性能。重要的是，我们还通过在 OAI 上训练模型并在 MOST 数据集上进行测试，研究了我们方法的可推广性。我们使用来自精确召回（PR）曲线的接收者操作特征曲线（ROC AUC）和平均精度（AP）来比较结果。
我们发现自适应 ROI 可提高分类性能（OA 与非 OA），优于常用的标准 ROI（AUC 提高高达 9%）。我们还观察到，在所有纹理参数中，局部二值模式（LBP）在所有设置下的性能最佳，最佳 AUC 为 0.840[0.825，0.852]，相应的 AP 为 0.804[0.786，0.820]。
与目前的先进方法相比，我们的结果表明，提出的骨皮质下骨纹理分析自适应 ROI 方法可以提高检测放射学 OA 存在的诊断性能。
