A Multi-Task Deep Learning Method for Detection of Meniscal Tears in MRI Data from the Osteoarthritis Initiative Database

Abstract

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.

Development of convolutional neural network model for diagnosing meniscus tear using magnetic resonance image

“…To the best of our knowledge, four previous studies have evaluated the diagnostic efficacy of the DL model for detecting meniscal tears on knee MRI [ 18 – 21 ]. In 2018, Bien et al developed a CNN model using 1370 cases of knee MRI (coronal, sagittal, and axial MR images; meniscus tear, 397) [ 18 ].…”

“…The AUC value for diagnosing medial meniscal tears was 0.93 and that for lateral meniscal tears was 0.84. Most recently, in 2021, Tack et al used 2399 sagittal 3-dimensional MRI scans from the publicly available database of the Osteoarthritis Initiative [ 21 ]. The AUC values for medial meniscal tears in the anterior horn, body, and posterior horn were 0.94, 0.93, and 0.93, respectively, whereas those for lateral meniscal tears were 0.96, 0.94, and 0.91, respectively.…”

“…The AUC values for medial meniscal tears in the anterior horn, body, and posterior horn were 0.94, 0.93, and 0.93, respectively, whereas those for lateral meniscal tears were 0.96, 0.94, and 0.91, respectively. Recent studies have reported an enhancement in the accuracy of DL models for diagnosing meniscal tears [ 20 , 21 ]. This can be attributed to the large number of MRI scans required.…”

“…A convolutional neural network (CNN) is a representative DL model that is advantageous, particularly in image recognition and classification [ 17 ]. Previous studies have shown that a CNN can be useful for determining the presence of meniscal tears in knee MRI images [ 18 – 21 ]. A CNN model that can differentiate tear location in the anterior horn, body, and posterior horn was recently developed [ 21 ].…”

“…Previous studies have shown that a CNN can be useful for determining the presence of meniscal tears in knee MRI images [ 18 – 21 ]. A CNN model that can differentiate tear location in the anterior horn, body, and posterior horn was recently developed [ 21 ]. We assumed that the CNN could be useful for classifying tear types (horizontal, complex, radial, and longitudinal tears) in addition to detecting meniscal tears.…”