Fully Automatic Analysis of Muscle B-Mode Ultrasound Images Based on the Deep Residual Shrinkage U-Net
Fully Automatic Analysis of Muscle B-Mode Ultrasound Images Based on the Deep Residual Shrinkage U-Net
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DOI：
10.3390/electronics11071093
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发表时间：
2022-03
期刊：
影响因子：
2.9
通讯作者：
Weimin Zheng;Linxueying Zhou;Qing-wei Chai;Jianguo Xu;Shangkun Liu
中科院分区：
文献类型：
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作者：
Weimin Zheng;Linxueying Zhou;Qing-wei Chai;Jianguo Xu;Shangkun Liu
肌肉超声图像的参数反映了肌肉的功能和状态。对肌肉疾病的诊断具有重要意义。由于人工标注费时费力，肌肉超声图像参数的自动标注成为一个研究课题。近年来，出现了许多应用图像处理和深度学习来自动分析肌肉超声图像的方法。然而，这些方法都有局限性，如非自动化，不适用于复杂噪声的图像，只能测量一个单一的参数。针对这些问题，提出了一种基于图像分割的全自动肌肉超声图像分析方法。该方法基于深度残差收缩U网（RS-Unet）对超声图像进行精确分割。与现有的方法相比，该方法的精度有了很大的提高。羽状角、肌束长度和肌肉厚度的平均差异分别为0.09°、0.4mm和0.63mm。实验结果表明，该方法实现了肌肉参数的精确测量，具有较好的稳定性和鲁棒性。
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.