Tack Alexander, Shestakov Alexey, Lüdke David, Zachow Stefan
Dept. for Visual and Data-Centric Computing, Zuse Institute Berlin, Berlin, Germany.
Charité-University Medicine, Berlin, Germany.
Front Bioeng Biotechnol. 2021 Dec 2;9:747217. doi: 10.3389/fbioe.2021.747217. eCollection 2021.
We present a novel and computationally efficient method for the detection of meniscal tears in Magnetic Resonance Imaging (MRI) data. Our method is based on a Convolutional Neural Network (CNN) that operates on complete 3D MRI scans. Our approach detects the presence of meniscal tears in three anatomical sub-regions (anterior horn, body, posterior horn) for both the Medial Meniscus (MM) and the Lateral Meniscus (LM) individually. For optimal performance of our method, we investigate how to preprocess the MRI data and how to train the CNN such that only relevant information within a Region of Interest (RoI) of the data volume is taken into account for meniscal tear detection. We propose meniscal tear detection combined with a bounding box regressor in a multi-task deep learning framework to let the CNN implicitly consider the corresponding RoIs of the menisci. We evaluate the accuracy of our CNN-based meniscal tear detection approach on 2,399 Double Echo Steady-State (DESS) MRI scans from the Osteoarthritis Initiative database. In addition, to show that our method is capable of generalizing to other MRI sequences, we also adapt our model to Intermediate-Weighted Turbo Spin-Echo (IW TSE) MRI scans. To judge the quality of our approaches, Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) values are evaluated for both MRI sequences. For the detection of tears in DESS MRI, our method reaches AUC values of 0.94, 0.93, 0.93 (anterior horn, body, posterior horn) in MM and 0.96, 0.94, 0.91 in LM. For the detection of tears in IW TSE MRI data, our method yields AUC values of 0.84, 0.88, 0.86 in MM and 0.95, 0.91, 0.90 in LM. In conclusion, the presented method achieves high accuracy for detecting meniscal tears in both DESS and IW TSE MRI data. Furthermore, our method can be easily trained and applied to other MRI sequences.
我们提出了一种新颖且计算高效的方法，用于在磁共振成像（MRI）数据中检测半月板撕裂。我们的方法基于一个对完整3D MRI扫描进行操作的卷积神经网络（CNN）。我们的方法分别在半月板的三个解剖子区域（前角、体部、后角）中检测内侧半月板（MM）和外侧半月板（LM）是否存在撕裂。为了使我们的方法达到最佳性能，我们研究了如何对MRI数据进行预处理以及如何训练CNN，以便在半月板撕裂检测中仅考虑数据体感兴趣区域（RoI）内的相关信息。我们在多任务深度学习框架中提出了结合边界框回归器的半月板撕裂检测方法，以使CNN隐式地考虑半月板的相应RoI。我们在来自骨关节炎倡议数据库的2399例双回波稳态（DESS）MRI扫描上评估了基于CNN的半月板撕裂检测方法的准确性。此外，为了表明我们的方法能够推广到其他MRI序列，我们还将模型应用于中等加权快速自旋回波（IW TSE）MRI扫描。为了评判我们方法的质量，我们针对两种MRI序列评估了受试者工作特征（ROC）曲线和曲线下面积（AUC）值。对于DESS MRI中撕裂的检测，我们的方法在MM中前角、体部、后角的AUC值分别为0.94、0.93、0.93，在LM中分别为0.96、0.94、0.91。对于IW TSE MRI数据中撕裂的检测，我们的方法在MM中的AUC值分别为0.84、0.88、0.86，在LM中分别为0.95、0.91、0.90。总之，所提出的方法在检测DESS和IW TSE MRI数据中的半月板撕裂方面都达到了高精度。此外，我们的方法易于训练并可应用于其他MRI序列。
