Orthpoeadic Medical Center, Jilin University Second Hospital, Changchun, Jilin, China (mainland).
Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China (mainland).
Med Sci Monit. 2022 Jun 14;28:e936733. doi: 10.12659/MSM.936733.
BACKGROUND We aimed to develop and evaluate a deep learning-based method for fully automatic segmentation of knee joint MR imaging and quantitative computation of knee osteoarthritis (OA)-related imaging biomarkers. MATERIAL AND METHODS This retrospective study included 843 volumes of proton density-weighted fat suppression MR imaging. A convolutional neural network segmentation method with multiclass gradient harmonized Dice loss was trained and evaluated on 500 and 137 volumes, respectively. To assess potential morphologic biomarkers for OA, the volumes and thickness of cartilage and meniscus, and minimal joint space width (mJSW) were automatically computed and compared between 128 OA and 162 control data. RESULTS The CNN segmentation model produced reasonably high Dice coefficients, ranging from 0.948 to 0.974 for knee bone compartments, 0.717 to 0.809 for cartilage, and 0.846 for both lateral and medial menisci. The OA-related biomarkers computed from automatic knee segmentation achieved strong correlation with those from manual segmentation: average intraclass correlations of 0.916, 0.899, and 0.876 for volume and thickness of cartilage, meniscus, and mJSW, respectively. Volume and thickness measurements of cartilage and mJSW were strongly correlated with knee OA progression. CONCLUSIONS We present a fully automatic CNN-based knee segmentation system for fast and accurate evaluation of knee joint images, and OA-related biomarkers such as cartilage thickness and mJSW were reliably computed and visualized in 3D. The results show that the CNN model can serve as an assistant tool for radiologists and orthopedic surgeons in clinical practice and basic research.
我们旨在开发并评估一种基于深度学习的方法，用于膝关节磁共振成像的全自动分割和定量计算与骨关节炎（OA）相关的影像学生物标志物。
本回顾性研究纳入了 843 例质子密度加权脂肪抑制磁共振成像容积。使用多类别梯度协调 Dice 损失的卷积神经网络分割方法分别在 500 例和 137 例容积上进行训练和评估。为了评估潜在的与 OA 相关的形态学生物标志物，我们自动计算并比较了 128 例 OA 患者和 162 例对照数据的软骨和半月板容积和厚度以及最小关节间隙宽度（mJSW）。
CNN 分割模型产生了相当高的 Dice 系数，膝关节骨分割的范围为 0.948 至 0.974，软骨为 0.717 至 0.809，内外侧半月板为 0.846。从自动膝关节分割中计算出的与 OA 相关的生物标志物与手动分割具有很强的相关性：软骨、半月板和 mJSW 的体积和厚度的平均组内相关系数分别为 0.916、0.899 和 0.876。软骨和 mJSW 的体积和厚度测量与膝关节 OA 进展具有很强的相关性。
我们提出了一种全自动基于 CNN 的膝关节分割系统，用于快速准确地评估膝关节图像，以及可靠地计算和可视化与 OA 相关的生物标志物，如软骨厚度和 mJSW。结果表明，该 CNN 模型可以作为放射科医生和骨科医生在临床实践和基础研究中的辅助工具。
