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B-mode ultrasound is commonly used to image musculoskeletal tissues, but onemajor bottleneck is data interpretation, and analyses of muscle thickness,pennation angle and fascicle length are often still performed manually. In thisstudy we trained deep neural networks (based on U-net) to detect musclefascicles and aponeuroses using a set of labelled musculoskeletal ultrasoundimages. We then compared neural network predictions on new, unseen images tothose obtained via manual analysis and two existing semi/automated analysisapproaches (SMA and Ultratrack). With a GPU, inference time for a single imagewith the new approach was around 0.7s, compared to 4.6s with a CPU. Our methoddetects the locations of the superficial and deep aponeuroses, as well asmultiple fascicle fragments per image. For single images, the method gavesimilar results to those produced by a non-trainable automated method (SMA;mean difference in fascicle length: 1.1 mm) or human manual analysis (meandifference: 2.1 mm). Between-method differences in pennation angle were within1$^\circ$, and mean differences in muscle thickness were less than 0.2 mm.Similarly, for videos, there was strong overlap between the results producedwith Ultratrack and our method, with a mean ICC of 0.73, despite the fact thatthe analysed trials included hundreds of frames. Our method is fully automatedand open source, and can estimate fascicle length, pennation angle and musclethickness from single images or videos, as well as from multiple superficialmuscles. We also provide all necessary code and training data for custom modeldevelopment.

Fully automated analysis of muscle architecture from B-mode ultrasound images with deep learning