Arthritis Research UK Centre for Epidemiology, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.
Centre for Imaging Sciences, Division of Informatics, Imaging and Data Science, University of Manchester, Manchester, United Kingdom.
Magn Reson Med. 2019 May;81(5):3056-3064. doi: 10.1002/mrm.27633. Epub 2019 Feb 15.
Synovitis is common in knee osteoarthritis and is associated with both knee pain and progression of disease. Semiautomated methods have been developed for quantitative assessment of structure in knee osteoarthritis. Our aims were to apply a novel semiautomated assessment method using 3D active appearance modeling for the quantification of synovial tissue volume (STV) and to compare its performance with conventional manual segmentation.
Thirty-two sagittal T -weighted fat-suppressed contrast-enhanced MRIs were assessed for STV by a single observer using 1) manual segmentation and 2) a semiautomated approach. We compared the STV analysis using the semiautomated and manual segmentation methods, including the time taken to complete the assessments. We also examined the reliability of STV assessment using the semiautomated method in a subset of 12 patients who had participated in a clinical trial of vitamin D therapy in knee osteoarthritis.
There was no significant difference in STV using the semiautomated quantitative method compared to manual segmentation, mean difference = 207.2 mm (95% confidence interval -895.2 to 1309.7). The semiautomated method was significantly quicker than manual segmentation (18 vs. 71 min). For the semiautomated method, intraobserver agreement was excellent (intraclass correlation coefficient (3,1) = 0.99) and interobserver agreement was very good (intraclass correlation coefficient (3,1) = 0.83).
We describe the application of a semiautomated method that is accurate, reliable, and quicker than manual segmentation for assessment of STV. The method may help increase efficiency of image assessment in large imaging studies and may also assist investigation of treatment efficacy in knee osteoarthritis.
滑膜炎是膝关节骨关节炎的常见表现，与膝关节疼痛和疾病进展均相关。目前已开发出用于膝关节骨关节炎结构定量评估的半自动方法。我们的目的是应用一种新的基于三维主动外观模型的半自动评估方法来量化滑膜组织体积（STV），并将其与传统的手动分割方法进行比较。
由一位观察者分别采用 1）手动分割和 2）半自动方法对 32 例矢状位 T1 加权脂肪抑制对比增强 MRI 进行滑膜组织体积（STV）评估。我们比较了半自动和手动分割方法的 STV 分析结果，包括完成评估所需的时间。我们还在 12 例曾参与膝关节骨关节炎维生素 D 治疗临床试验的患者亚组中评估了使用半自动方法评估 STV 的可靠性。
与手动分割相比，半自动定量方法的 STV 无显著差异，平均差值为 207.2mm（95%置信区间-895.2 至 1309.7）。半自动方法比手动分割明显更快（18 分钟比 71 分钟）。对于半自动方法，观察者内一致性极好（组内相关系数（3,1）=0.99），观察者间一致性非常好（组内相关系数（3,1）=0.83）。
我们描述了一种半自动方法的应用，该方法用于评估 STV 时准确、可靠且比手动分割更快。该方法可能有助于提高大型影像学研究中图像评估的效率，也可能有助于膝关节骨关节炎治疗效果的研究。
