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简单肌肉架构分析 (SMA):一个可用于 B 型超声扫描中自动测量的 ImageJ 宏工具。

Simple Muscle Architecture Analysis (SMA): An ImageJ macro tool to automate measurements in B-mode ultrasound scans.

机构信息

Department for Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway.

Neuromuscular Research Centre, Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland.

出版信息

PLoS One. 2020 Feb 12;15(2):e0229034. doi: 10.1371/journal.pone.0229034. eCollection 2020.

Abstract

In vivo measurements of muscle architecture (i.e. the spatial arrangement of muscle fascicles) are routinely included in research and clinical settings to monitor muscle structure, function and plasticity. 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. 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). Images are filtered in the spatial and frequency domains with built-in commands and external plugins to highlight aponeuroses and fascicles. Fascicle dominant orientation is then computed in regions of interest using the OrientationJ plugin. 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. Our test results illustrate the suitability of SMA to analyse images from superficial muscles acquired with a broad range of ultrasound settings.

摘要

在研究和临床环境中,通常会进行肌肉结构(即肌束的空间排列)的体内测量,以监测肌肉的结构、功能和可塑性。然而,在大多数情况下,这些测量都是手动进行的,更可靠和更高效的自动化方法要么完全缺乏,要么没有图像分析专业知识的人无法使用。在这项工作中,我们提出了一个 ImageJ 脚本,以自动执行超声图像中肌肉结构的整个分析过程:简单肌肉结构分析(SMA)。使用内置命令和外部插件对图像进行空域和频域滤波,以突出腱膜和肌束。然后使用 OrientationJ 插件在感兴趣区域计算肌束主导方向。手动或使用 SMA 进行的分析的 Bland-Altman 图表明,自动分析不会引起任何系统偏差,并且两种方法在整个测量范围内的一致性相等。我们的测试结果说明了 SMA 适用于分析使用广泛的超声设置获取的浅层肌肉图像。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0cd3/7015391/aa0f6f6339b3/pone.0229034.g001.jpg

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