3D 卷积神经网络在骨关节炎和前交叉韧带损伤患者的半月板和 PFJ 软骨形态退行性变的检测和严重程度分期中的应用。
3D convolutional neural networks for detection and severity staging of meniscus and PFJ cartilage morphological degenerative changes in osteoarthritis and anterior cruciate ligament subjects.
机构信息
Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.
Center of Digital Health Innovation (CDHI).
出版信息
J Magn Reson Imaging. 2019 Feb;49(2):400-410. doi: 10.1002/jmri.26246. Epub 2018 Oct 10.
BACKGROUND
Semiquantitative assessment of MRI plays a central role in musculoskeletal research; however, in the clinical setting MRI reports often tend to be subjective and qualitative. Grading schemes utilized in research are not used because they are extraordinarily time-consuming and unfeasible in clinical practice.
PURPOSE
To evaluate the ability of deep-learning models to detect and stage severity of meniscus and patellofemoral cartilage lesions in osteoarthritis and anterior cruciate ligament (ACL) subjects.
STUDY TYPE
Retrospective study aimed to evaluate a technical development.
POPULATION
In all, 1478 MRI studies, including subjects at various stages of osteoarthritis and after ACL injury and reconstruction.
FIELD STRENGTH/SEQUENCE: 3T MRI, 3D FSE CUBE.
ASSESSMENT
Automatic segmentation of cartilage and meniscus using 2D U-Net, automatic detection, and severity staging of meniscus and cartilage lesion with a 3D convolutional neural network (3D-CNN).
STATISTICAL TESTS
Receiver operating characteristic (ROC) curve, specificity and sensitivity, and class accuracy.
RESULTS
Sensitivity of 89.81% and specificity of 81.98% for meniscus lesion detection and sensitivity of 80.0% and specificity of 80.27% for cartilage were achieved. The best performances for staging lesion severity were obtained by including demographics factors, achieving accuracies of 80.74%, 78.02%, and 75.00% for normal, small, and complex large lesions, respectively.
DATA CONCLUSION
In this study we provide a proof of concept of a fully automated deep-learning pipeline that can identify the presence of meniscal and patellar cartilage lesions. This pipeline has also shown potential in making more in-depth examinations of lesion subjects for multiclass prediction and severity staging.
LEVEL OF EVIDENCE
2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:400-410.
背景
磁共振成像（MRI）的半定量评估在肌肉骨骼研究中起着核心作用；然而，在临床环境中，MRI 报告往往倾向于主观和定性。研究中使用的分级方案由于耗时且在临床实践中不可行，因此未被采用。
目的
评估深度学习模型在检测和分期骨关节炎和前交叉韧带（ACL）患者半月板和髌股软骨损伤严重程度方面的能力。
研究类型
旨在评估技术发展的回顾性研究。
人群
共有 1478 项 MRI 研究，包括处于不同阶段骨关节炎和 ACL 损伤及重建后的患者。
磁场强度/序列：3T MRI，3D FSE CUBE。
评估
使用 2D U-Net 自动分割软骨和半月板，使用 3D 卷积神经网络（3D-CNN）自动检测和严重程度分期半月板和软骨损伤。
统计检验
接收者操作特征（ROC）曲线、特异性和敏感性以及分类准确性。
结果
半月板损伤检测的敏感性为 89.81%，特异性为 81.98%，软骨的敏感性为 80.0%，特异性为 80.27%。通过纳入人口统计学因素，获得最佳的病变严重程度分期性能，正常、小和复杂大病变的准确性分别为 80.74%、78.02%和 75.00%。
数据结论
在这项研究中，我们提供了一个完全自动化的深度学习管道的概念验证，该管道可以识别半月板和髌股软骨损伤的存在。该管道还显示出在对病变进行更深入检查以进行多类预测和严重程度分期方面具有潜力。
证据水平
2 技术功效：第 2 阶段 J. 磁共振成像 2019；49：400-410。
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