College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China.
Ultrason Imaging. 2021 Mar;43(2):74-87. doi: 10.1177/0161734621989598.
In this study, an automatic pennation angle measuring approach based on deep learning is proposed. Firstly, the Local Radon Transform (LRT) is used to detect the superficial and deep aponeuroses on the ultrasound image. Secondly, a reference line are introduced between the deep and superficial aponeuroses to assist the detection of the orientation of muscle fibers. The Deep Residual Networks (Resnets) are used to judge the relative orientation of the reference line and muscle fibers. Then, reference line is revised until the line is parallel to the orientation of the muscle fibers. Finally, the pennation angle is obtained according to the direction of the detected aponeuroses and the muscle fibers. The angle detected by our proposed method differs by about 1° from the angle manually labeled. With a CPU, the average inference time for a single image of the muscle fibers with the proposed method is around 1.6 s, compared to 0.47 s for one of the image of a sequential image sequence. Experimental results show that the proposed method can achieve accurate and robust measurements of pennation angle.
本研究提出了一种基于深度学习的自动羽状角测量方法。首先，利用局部 Radon 变换（LRT）检测超声图像上的浅部和深部肌腱。其次，在深部和浅部肌腱之间引入参考线，以辅助检测肌纤维的方向。深度残差网络（Resnets）用于判断参考线和肌纤维的相对方向。然后，修正参考线，直到该线与肌纤维的方向平行。最后，根据检测到的肌腱和肌纤维的方向得到羽状角。我们提出的方法检测到的角度与手动标记的角度相差约 1°。使用 CPU，对于使用该方法的单个肌纤维图像，平均推理时间约为 1.6 s，而对于连续图像序列的单个图像，平均推理时间为 0.47 s。实验结果表明，该方法可以实现准确和稳健的羽状角测量。
