Original ultrasound images and Hough transformed images

The morphological characteristics of skeletal muscles, such as fascicle orientation, fascicle length, and muscle thickness, contain valuable mechanical information that aids in understanding muscle contractility and excitation due to commands from the central nervous system. Ultrasound (US) imaging, a non-invasive measurement technique, has been employed in clinical research to provide visualized images that capture morphological characteristics. However, accurately and efficiently detecting the fascicle in US images is challenging. In the current study, we employed computer vision techniques based on shallow/light neural networks (SNN) to detect the locations and orientations of fascicles in US images. To enhance the linear/tubular feature of the fascicle, we developed a weighted Hough transform algorithm to transform the original gray-scaled images to a weighted Hough space (WHS), which is expected to reduce the demand in layers for the neural network (NN) design. Compared to many baseline methods, including Vgg, ResNet, AlexNet, Unet, and UltraTrack, the proposed SNN was found to be more accurate in detection. Among the SNN methods, a single-layer convolutional neural network outperformed the others. Our study found that WHS usually improved the detection accuracy for SNN models, and the regression with an $\mathcal{L}_{2}$ regularization provided satisfactory detection even without WHS, which is suitable for real-time applications. Moreover, our proposed methods are robust to disturbances like data loss, time delay, and out-of-order, as they do not require the image frames to be closely interlinked or temporally related.  

The path of the file is stated in the code, which can be found here: https://github.com/XBao06093030/SCNN_for_Fascicle_Detection/blob/main/SC...