Knee menisci segmentation using convolutional neural networks: data from the Osteoarthritis Initiative
Knee menisci segmentation using convolutional neural networks: data from the Osteoarthritis Initiative
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DOI：
10.1016/j.joca.2018.02.907
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发表时间：
2018-05-01
影响因子：
7
通讯作者：
Zachow, S.
中科院分区：
文献类型：
--
作者：
Tack, A.;Mukhopadhyay, A.;Zachow, S.
目的：提出一种新的膝关节MRIs图像自动分割方法。评估骨关节炎（OA）的定量生物标志物估计theirs.Method：采用卷积神经网络结合统计形状模型的分割方法。对88个手动分割进行了准确性评价。计算半月板体积、胫骨覆盖和半月板挤出，并检验OA、关节间隙狭窄（JSN）和WOMAC疼痛组之间的差异。对600名受试者进行了计算机椎间盘突出和MRI骨关节炎膝关节评分（MOAKS）专家读数之间的相关性评价。在一组552名患者中检测了从基线到24个月预测影像学OA事件的生物标志物的适用性（184例发病OA，386例对照）进行条件Logistic回归分析。基线时，以骰子相似系数衡量的分割准确率为内侧髁（MM）83.8%，外侧髁（LM）88.9%，12个月随访时分别为83.1%和88.3%。关节炎病例的胫骨内侧覆盖率显著低于非关节炎病例。关节炎膝关节的内侧髁挤出显著更高。自动计算的内侧髁突挤压和专家的读数之间存在中度相关性（r = 0.44）。与对照组相比，OA病例的平均内侧髁突突出显著更大（1.16 +/- 0.93 mm vs 0.83 +/- 0.92 mm; P < 0.05）。通过与专家的读数进行比较以及分析OA、JSN和WOMAC疼痛组之间的差异，验证了我们的生物标志物。经证实，内侧髁挤出是OA事件的预测因子。(C)2018国际骨关节炎研究学会。由爱思唯尔有限公司出版。保留所有权利。
Objective: To present a novel method for automated segmentation of knee menisci from MRIs. To evaluate quantitative meniscal biomarkers for osteoarthritis (OA) estimated thereof.Method: A segmentation method employing convolutional neural networks in combination with statistical shape models was developed. Accuracy was evaluated on 88 manual segmentations. Meniscal volume, tibial coverage, and meniscal extrusion were computed and tested for differences between groups of OA, joint space narrowing (JSN), and WOMAC pain. Correlation between computed meniscal extrusion and MRI Osteoarthritis Knee Score (MOAKS) experts' readings was evaluated for 600 subjects. Suitability of biomarkers for predicting incident radiographic OA from baseline to 24 months was tested on a group of 552 patients (184 incident OA, 386 controls) by performing conditional logistic regression.Results: Segmentation accuracy measured as dice similarity coefficient was 83.8% for medial menisci (MM) and 88.9% for lateral menisci (LM) at baseline, and 83.1% and 88.3% at 12-month follow-up. Medial tibial coverage was significantly lower for arthritic cases compared to non-arthritic ones. Medial meniscal extrusion was significantly higher for arthritic knees. A moderate correlation between automatically computed medial meniscal extrusion and experts' readings was found (r = 0.44). Mean medial meniscal extrusion was significantly greater for incident OA cases compared to controls (1.16 +/- 0.93 mm vs 0.83 +/- 0.92 mm; P < 0.05).Conclusion: Especially for medial menisci an excellent segmentation accuracy was achieved. Our meniscal biomarkers were validated by comparison to experts' readings as well as analysis of differences w.r.t groups of OA, JSN, and WOMAC pain. It was confirmed that medial meniscal extrusion is a predictor for incident OA. (C) 2018 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.