Dual Stream Feature Fusion 3D Network for supraspinatus tendon tear classification

Abstract

The classification of medical images is of significant importance for computer-aided diagnosis. Supraspinatus tendon tear is a common clinical condition. Classifying the severity of supraspinatus tendon tears accurately aids in the selection of surgical techniques and postoperative rehabilitation. While some studies have classified supraspinatus tendon tears, existing methods lack detailed classification. Inaccurate and insufficiently detailed classification can lead to errors in the selection of surgical techniques, thereby affecting patient treatment and rehabilitation. In addition, the computational complexity of traditional 3D classification models is too high. In this study, we conducted a detailed 6-class classification of the supraspinatus tendon tears for the first time. We propose a novel 3D model for classifying supraspinatus tendon tears, the Dual Stream Feature Fusion 3D Network (DSFF-3DNet). To accelerate the extraction of the Region of Interest (ROI), we trained the Yolov9 model to identify the supraspinatus tendon and save the Yolo label. DSFF-3DNet comprises three stages: feature extraction, feature enhancement, and classification. We performed data augmentation, training, validation and internal testing on a dataset with 1014 patients, and tested on two independent external test sets. DSFF-3DNet achieved AUCs of 97.88, 88.06, and 84.47 on the internal test set and the two external test sets, respectively, surpassing the best-performing traditional models on these three test sets by 3.51%, 9.25%, and 9.38% across these test sets. Ablation experiments demonstrated the individual contributions of each module in DSFF-3DNet, and significance difference tests showed that the performance improvements were statistically significant (p$<$0.05).