![]() Python), it is easy to learn and use, and was chosen with the goal of developing a tool customisable by most users. Although this scripting language is less powerful than others (e.g. ![]() The analysis script is written in ImageJ 1.x macro language and runs in Fiji. Workflow of the SMA (Simple Muscle Architecture Analysis) macro. The analysis method is summarised in Fig 1.įig 1. The sample images used to test the present method were collected in previous projects, for which the subjects gave permission to use images by signing an informed consent and ethical approval was granted by the ethical committee of the Norwegian School of Sport Sciences. aponeuroses or fascicles) and improve their detection and registration. One of the originalities of the present approach is the implementation of filters in the frequency domain to isolate objects of interest (i.e. A challenge associated with automatic segmentation of fascicles and aponeuroses is therefore to discriminate between signals from all echogenic regions (and artefactual echoes) and those from objects of interest. fat, other types of connective tissue, blood vessels). In addition to the connective tissue surrounding bundles of muscle fibres (perimysium) and whole muscles (aponeuroses), ultrasound scans of muscle architecture acquired in brightness (B) mode show several types of echogenic tissues (e.g. In the following sections we present an overview of the method, as well as examples of analysis output and a range of test metrics.Ī preprint version of this paper was first published at the following address. We provide full instructions for using the method, and no previous programming experience is required. The method consists of a single macro in Fiji, which is a distribution of ImageJ, and is open source software that is commonly used to process ultrasound images. In this study we present Simple Muscle Architecture Analysis (SMA), a fully automated method of analysing muscle architectural parameters from individual images or collections of images. Collectively, these factors limit the widespread use of existing methods. However, these efforts have been fragmented, and suffer from a number of limitations: they often focus on analysing a single parameter of interest most publications do not reveal specific details of how to implement the method some methods rely on software that require expensive licence fees the majority of methods are only semi-automated, requiring manual, subjective interpretation of at least some images and often tracking methods involve complex mathematics and require computer programming experience. In recent years, efforts have been made to automate parts of this process. The images generated by this method are complex and require a great deal of time and effort from practitioners to interpret and extract meaning from them. Musculoskeletal ultrasound imaging is used in a wide range of fields, including the study of muscle and tendon function, the effects of training on muscle architecture, and in the study of architectural parameters in different clinical populations. Our test results illustrate the suitability of SMA to analyse images from superficial muscles acquired with a broad range of ultrasound settings. Bland-Altman plots of analyses performed manually or with SMA indicate that the automated analysis does not induce any systematic bias and that both methods agree equally through the range of measurements. Fascicle dominant orientation is then computed in regions of interest using the OrientationJ plugin. Images are filtered in the spatial and frequency domains with built-in commands and external plugins to highlight aponeuroses and fascicles. In this work, we propose an ImageJ script to automate the entire analysis process of muscle architecture in ultrasound images: Simple Muscle Architecture Analysis (SMA). However, in most cases such measurements are performed manually, and more reliable and time-efficient automated methods are either lacking completely, or are inaccessible to those without expertise in image analysis. the spatial arrangement of muscle fascicles) are routinely included in research and clinical settings to monitor muscle structure, function and plasticity. In vivo measurements of muscle architecture (i.e.
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