Shen Wuyi, Zhang Yuancheng, Zhang Haoyu, Zhong Hui, Wan Mingxi
Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Key Laboratory of Biomedical Information Engineering of Ministry of Education, Xi'an, Shaanxi, China.
Ultrason Imaging. 2025 Jul;47(3-4):134-152. doi: 10.1177/01617346251330111. Epub 2025 Jun 20.
B-line artifacts in lung ultrasound, pivotal for diagnosing pulmonary conditions, warrant automated recognition to enhance diagnostic accuracy. In this paper, a lung ultrasound B-line vertical artifact identification method based on radio frequency (RF) signal was proposed. B-line regions were distinguished from non-B-line regions by inputting multiple characteristic parameters into nonlinear support vector machine (SVM). Six characteristic parameters were evaluated, including permutation entropy, information entropy, kurtosis, skewness, Nakagami shape factor, and approximate entropy. Following the evaluation that demonstrated the performance differences in parameter recognition, Principal Component Analysis (PCA) was utilized to reduce the dimensionality to a four-dimensional feature set for input into a nonlinear Support Vector Machine (SVM) for classification purposes. Four types of experiments were conducted: a sponge with dripping water model, gelatin phantoms containing either glass beads or gelatin droplets, and in vivo experiments. By employing precise feature selection and analyzing scan lines rather than full images, this approach significantly reduced the dependency on large image datasets without compromising discriminative accuracy. The method exhibited performance comparable to contemporary image-based deep learning approaches, which, while highly effective, typically necessitate extensive data for training and require expert annotation of large datasets to establish ground truth. Owing to the optimized architecture of our model, efficient sample recognition was achieved, with the capability to process between 27,000 and 33,000 scan lines per second (resulting in a frame rate exceeding 100 FPS, with 256 scan lines per frame), thus supporting real-time analysis. The results demonstrate that the accuracy of the method to classify a scan line as belonging to a B-line region was up to 88%, with sensitivity reaching up to 90%, specificity up to 87%, and an F1-score up to 89%. This approach effectively reflects the performance of scan line classification pertinent to B-line identification. Our approach reduces the reliance on large annotated datasets, thereby streamlining the preprocessing phase.
肺部超声中的B线伪像对肺部疾病的诊断至关重要，需要自动识别以提高诊断准确性。本文提出了一种基于射频（RF）信号的肺部超声B线垂直伪像识别方法。通过将多个特征参数输入非线性支持向量机（SVM），将B线区域与非B线区域区分开来。评估了六个特征参数，包括排列熵、信息熵、峰度、偏度、 Nakagami形状因子和近似熵。在评估表明参数识别中的性能差异之后，利用主成分分析（PCA）将维度降低到四维特征集，以输入非线性支持向量机（SVM）进行分类。进行了四种类型的实验：滴水海绵模型、含有玻璃珠或明胶液滴的明胶仿体以及体内实验。通过采用精确的特征选择并分析扫描线而非完整图像，该方法在不影响判别准确性的情况下，显著降低了对大型图像数据集的依赖性。该方法表现出与当代基于图像的深度学习方法相当的性能，基于图像的深度学习方法虽然非常有效，但通常需要大量数据进行训练，并且需要对大型数据集进行专家注释以建立基准真值。由于我们模型的优化架构，实现了高效的样本识别，能够每秒处理27000至33000条扫描线（导致帧率超过100 FPS，每帧256条扫描线），从而支持实时分析。结果表明，该方法将扫描线分类为属于B线区域的准确率高达88%，灵敏度高达90%，特异性高达87%，F1分数高达89%。该方法有效地反映了与B线识别相关的扫描线分类性能。我们的方法减少了对大型注释数据集的依赖，从而简化了预处理阶段。
