Automatic Analysis of Transverse Musculoskeletal Ultrasound Images Based on the Multi-Task Learning Model

Musculoskeletal ultrasound imaging is an important basis for the early screening and accurate treatment of muscle disorders. It allows the observation of muscle status to screen for underlying neuromuscular diseases including myasthenia gravis, myotonic dystrophy, and ankylosing muscular dystrophy. Due to the complexity of skeletal muscle ultrasound image noise, it is a tedious and time-consuming process to analyze. Therefore, we proposed a multi-task learning-based approach to automatically segment and initially diagnose transverse musculoskeletal ultrasound images. The method implements muscle cross-sectional area (CSA) segmentation and abnormal muscle classification by constructing a multi-task model based on multi-scale fusion and attention mechanisms (MMA-Net). The model exploits the correlation between tasks by sharing a part of the shallow network and adding connections to exchange information in the deep network. The multi-scale feature fusion module and attention mechanism were added to MMA-Net to increase the receptive field and enhance the feature extraction ability. Experiments were conducted using a total of 1827 medial gastrocnemius ultrasound images from multiple subjects. Ten percent of the samples were randomly selected for testing, 10% as the validation set, and the remaining 80% as the training set. The results show that the proposed network structure and the added modules are effective. Compared with advanced single-task models and existing analysis methods, our method has a better performance at classification and segmentation. The mean Dice coefficients and IoU of muscle cross-sectional area segmentation were 96.74% and 94.10%, respectively. The accuracy and recall of abnormal muscle classification were 95.60% and 94.96%. The proposed method achieves convenient and accurate analysis of transverse musculoskeletal ultrasound images, which can assist physicians in the diagnosis and treatment of muscle diseases from multiple perspectives

Fully Automatic Analysis of Muscle B-Mode Ultrasound Images Based on the Deep Residual Shrinkage U-Net

The parameters of muscle ultrasound images reflect the function and state of muscles. They are of great significance to the diagnosis of muscle diseases. Because manual labeling is time-consuming and laborious, the automatic labeling of muscle ultrasound image parameters has become a research topic. In recent years, there have been many methods that apply image processing and deep learning to automatically analyze muscle ultrasound images. However, these methods have limitations, such as being non-automatic, not applicable to images with complex noise, and only being able to measure a single parameter. This paper proposes a fully automatic muscle ultrasound image analysis method based on image segmentation to solve these problems. This method is based on the Deep Residual Shrinkage U-Net(RS-Unet) to accurately segment ultrasound images. Compared with the existing methods, the accuracy of our method shows a great improvement. The mean differences of pennation angle, fascicle length and muscle thickness are about 0.09°, 0.4 mm and 0.63 mm, respectively. Experimental results show that the proposed method realizes the accurate measurement of muscle parameters and exhibits stability and robustness

Fully Automatic Analysis of Muscle B-Mode Ultrasound Images Based on the Deep Residual Shrinkage U-Net

The parameters of muscle ultrasound images reflect the function and state of muscles. They are of great significance to the diagnosis of muscle diseases. Because manual labeling is time-consuming and laborious, the automatic labeling of muscle ultrasound image parameters has become a research topic. In recent years, there have been many methods that apply image processing and deep learning to automatically analyze muscle ultrasound images. However, these methods have limitations, such as being non-automatic, not applicable to images with complex noise, and only being able to measure a single parameter. This paper proposes a fully automatic muscle ultrasound image analysis method based on image segmentation to solve these problems. This method is based on the Deep Residual Shrinkage U-Net(RS-Unet) to accurately segment ultrasound images. Compared with the existing methods, the accuracy of our method shows a great improvement. The mean differences of pennation angle, fascicle length and muscle thickness are about 0.09°, 0.4 mm and 0.63 mm, respectively. Experimental results show that the proposed method realizes the accurate measurement of muscle parameters and exhibits stability and robustness