Paproki A, Engstrom C, Chandra S S, Neubert A, Fripp J, Crozier S
The Australian e-Health Research Centre, CSIRO Computational Informatics, Royal Brisbane and Women's Hospital, Herston, QLD 4029, Australia; School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, QLD 4027, Australia.
School of Human Movement Studies, The University of Queensland, St Lucia, QLD 4072, Australia.
Osteoarthritis Cartilage. 2014 Sep;22(9):1259-70. doi: 10.1016/j.joca.2014.06.029. Epub 2014 Jul 8.
To validate an automatic scheme for the segmentation and quantitative analysis of the medial meniscus (MM) and lateral meniscus (LM) in magnetic resonance (MR) images of the knee.
We analysed sagittal water-excited double-echo steady-state MR images of the knee from a subset of the Osteoarthritis Initiative (OAI) cohort. The MM and LM were automatically segmented in the MR images based on a deformable model approach. Quantitative parameters including volume, subluxation and tibial-coverage were automatically calculated for comparison (Wilcoxon tests) between knees with variable radiographic osteoarthritis (rOA), medial and lateral joint space narrowing (mJSN, lJSN) and pain. Automatic segmentations and estimated parameters were evaluated for accuracy using manual delineations of the menisci in 88 pathological knee MR examinations at baseline and 12 months time-points.
The median (95% confidence-interval (CI)) Dice similarity index (DSI) (2 ∗|Auto ∩ Manual|/(|Auto|+|Manual|)∗ 100) between manual and automated segmentations for the MM and LM volumes were 78.3% (75.0-78.7), 83.9% (82.1-83.9) at baseline and 75.3% (72.8-76.9), 83.0% (81.6-83.5) at 12 months. Pearson coefficients between automatic and manual segmentation parameters ranged from r = 0.70 to r = 0.92. MM in rOA/mJSN knees had significantly greater subluxation and smaller tibial-coverage than no-rOA/no-mJSN knees. LM in rOA knees had significantly greater volumes and tibial-coverage than no-rOA knees.
Our automated method successfully segmented the menisci in normal and osteoarthritic knee MR images and detected meaningful morphological differences with respect to rOA and joint space narrowing (JSN). Our approach will facilitate analyses of the menisci in prospective MR cohorts such as the OAI for investigations into pathophysiological changes occurring in early osteoarthritis (OA) development.
验证一种用于膝关节磁共振（MR）图像中内侧半月板（MM）和外侧半月板（LM）分割及定量分析的自动方案。
我们分析了骨关节炎倡议（OAI）队列中一部分人群的膝关节矢状面水激发双回波稳态MR图像。基于可变形模型方法在MR图像中自动分割MM和LM。自动计算包括体积、半脱位和胫骨覆盖等定量参数，以便对具有不同放射学骨关节炎（rOA）、内侧和外侧关节间隙变窄（mJSN、lJSN）及疼痛的膝关节进行比较（Wilcoxon检验）。在88例病理性膝关节MR检查的基线和12个月时间点，通过半月板的手动勾勒来评估自动分割和估计参数的准确性。
MM和LM体积的手动与自动分割之间的中位（95%置信区间（CI））骰子相似性指数（DSI）（2∗|自动∩手动|/（|自动|+|手动|）∗100）在基线时分别为78.3%（75.0 - 78.7）、83.9%（82.1 - 83.9），在12个月时分别为75.3%（72.8 - 76.9）、83.0%（81.6 - 83.5）。自动与手动分割参数之间的皮尔逊系数范围为r = 0.70至r = 0.92。与无rOA/无mJSN的膝关节相比，rOA/mJSN膝关节中的MM半脱位明显更大，胫骨覆盖更小。与无rOA的膝关节相比，rOA膝关节中的LM体积和胫骨覆盖明显更大。
我们的自动化方法成功地在正常和骨关节炎膝关节MR图像中分割了半月板，并检测到了与rOA和关节间隙变窄（JSN）相关的有意义的形态学差异。我们的方法将有助于在前瞻性MR队列（如OAI）中对半月板进行分析，以研究早期骨关节炎（OA）发展过程中发生的病理生理变化。
