Inflammation of the long head of the biceps tendon is a common cause of shoulder pain. Bicipital peritendinous effusion (BPE) is the most common biceps tendon abnormality and is related to various shoulder injuries. Physicians usually use ultrasound imaging to grade the inflammation severity of the long head of the biceps tendon. However, obtaining a clear and accurate ultrasound image is difficult for inexperienced attending physicians. To reduce physicians' workload and avoid errors, an automated BPE recognition system was developed in this study for classifying inflammation into the following categories-normal and mild, moderate, and severe. An ultrasound image serves as the input in the proposed system; the system determines whether the ultrasound image contains biceps. If the image depicts biceps, then the system predicts BPE severity. In this study, two crucial methods were used for solving problems associated with computer-aided detection. First, the faster regions with convolutional neural network (faster R-CNN) used to extract the region of interest (ROI) area identification to evaluate the influence of dataset scale and spatial image context on performance. Second, various CNN architectures were evaluated and explored. Model performance was analyzed by using various network configurations, parameters, and training sample sizes. The proposed system was used for three-class BPE classification and achieved 75% accuracy. The results obtained for the proposed system were determined to be comparable to those of other related state-of-the-art methods.

中文翻译：

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使用深度学习在二尖瓣腹膜积液的超声成像中对炎症程度进行分级。

二头肌腱长头的炎症是肩痛的常见原因。肱二头肌周积液（BPE）是最常见的二头肌肌腱异常，与各种肩部受伤有关。医师通常使用超声成像来对二头肌腱长头的炎症严重程度进行分级。但是，对于没有经验的主治医师，很难获得清晰准确的超声图像。为了减少医生的工作量并避免错误，本研究开发了一种自动BPE识别系统，用于将炎症分为正常和轻度，中度和严重以下几类。超声图像作为建议系统的输入；系统确定超声图像是否包含二头肌。如果图像显示二头肌，则系统会预测BPE严重程度。在这项研究中，使用了两种关键方法来解决与计算机辅助检测相关的问题。首先，具有卷积神经网络的更快区域（更快的R-CNN）用于提取感兴趣区域（ROI）区域标识，以评估数据集规模和空间图像上下文对性能的影响。其次，评估和探索了各种CNN体系结构。通过使用各种网络配置，参数和训练样本量来分析模型性能。提出的系统用于三类BPE分类，并达到75％的准确度。确定该提议系统获得的结果可与其他相关技术水平的结果进行比较。使用卷积神经网络的更快区域（更快的R-CNN）来提取感兴趣区域（ROI）区域标识，以评估数据集规模和空间图像上下文对性能的影响。其次，评估和探索了各种CNN体系结构。通过使用各种网络配置，参数和训练样本量来分析模型性能。提出的系统用于三类BPE分类，并达到75％的准确度。确定该提议系统获得的结果可与其他相关技术水平的结果进行比较。使用卷积神经网络（更快的R-CNN）提取更快的区域，以提取感兴趣区域（ROI）区域标识，以评估数据集规模和空间图像上下文对性能的影响。其次，评估和探索了各种CNN体系结构。通过使用各种网络配置，参数和训练样本量来分析模型性能。提出的系统用于三类BPE分类，并达到75％的准确度。确定该提议系统获得的结果与其他相关技术水平的结果可比。评估和探索了各种CNN架构。通过使用各种网络配置，参数和训练样本量来分析模型性能。提出的系统用于三类BPE分类，并达到75％的准确度。确定该提议系统获得的结果可与其他相关技术水平的结果进行比较。评估和探索了各种CNN架构。通过使用各种网络配置，参数和训练样本量来分析模型性能。提出的系统用于三类BPE分类，并达到75％的准确度。确定该提议系统获得的结果可与其他相关技术水平的结果进行比较。