Research involving B-mode ultrasound imaging often requires user defined regions of interest (ROIs) for analysis, traditionally drawn/selected by a trained operator. This manual process is incredibly time consuming and subjective. Here, we propose a fast and simple method of detecting the average location of aponeurosis layers in ultrasound images of the upper trapezius to place a rectangular ROI for quantitative image analysis. A total of 56 B-mode ultrasound images were analyzed, where rectangular ROIs were manually placed in the skeletal muscle by two trained operators. Interoperator agreement was determined between the ROI border locations using intercorrelation coefficient (ICC). Next, our automatic algorithm was applied (image thresholding, binary masking, and pixel intensity peak detection), estimating the mean position of both aponeurosis layers for rectangular ROI placement. The automatic estimation method was compared with the manual (visual) method by various statistics (t test, linear correlation, Bland-Altman plot). The performance was also evaluated under additive noise conditions (Speckle). Finally, agreement of the overlapping ROI area between the manual and automatic methods was also computed. Performance of the automatic method compared with manual placement was excellent for both the superficial and deep ROI borders, performing consistently even with additive noise (error <0.674 ± 1.69 mm). Manual measurements indicated excellent consensus (ICC = 0.902) between operators. The overlapping area between the manual and automatic measurements demonstrated good agreement (90.65 ± 11.3%). With constraints, our method is robust even under large levels of noise addition making the automatic algorithm an acceptable replacement for manual ROI placement in the upper trapezius.

中文翻译：

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B 型超声图像中上斜方肌中的自动 ROI 放置

涉及 B 模式超声成像的研究通常需要用户定义的感兴趣区域 (ROI) 进行分析，传统上由受过训练的操作员绘制/选择。这个手动过程非常耗时且主观。在这里，我们提出了一种快速简单的方法来检测上斜方肌超声图像中腱膜层的平均位置，以放置矩形 ROI 进行定量图像分析。总共分析了 56 张 B 型超声图像，其中由两名训练有素的操作员手动将矩形 ROI 放置在骨骼肌中。使用互相关系数 (ICC) 确定 ROI 边界位置之间的操作员协议。接下来，应用我们的自动算法（图像阈值、二进制掩码和像素强度峰值检测），估计矩形 ROI 放置的两个腱膜层的平均位置。通过各种统计（t 检验、线性相关、Bland-Altman 图）将自动估计方法与手动（视觉）方法进行比较。还在加性噪声条件（散斑）下评估了性能。最后，还计算了手动和自动方法之间重叠 ROI 区域的一致性。与手动放置相比，自动方法的性能在浅层和深层 ROI 边界方面都非常出色，即使存在附加噪声（误差 <0.674 ± 1.69 毫米）也能保持一致。手动测量表明操作者之间的一致性很好 (ICC = 0.902)。手动和自动测量之间的重叠区域表现出良好的一致性 (90.65 ± 11.3%)。有约束，