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Abstract：
In recent years, contrast-enhanced ultrasonography (CEUS) has been used for various applications in breast diagnosis. The superiority of CEUS over conventional B-mode imaging in the ultrasound diagnosis of the breast lesions in clinical practice has been widely confirmed. On the other hand, there have been many proposals for computer-aided diagnosis of breast lesions on B-mode ultrasound images, but few for CEUS. We propose a semi-automatic classification method based on machine learning in CEUS of breast lesions.
The proposed method extracts spatial and temporal features from CEUS videos and breast tumors are classified as benign or malignant using linear support vector machines (SVM) with combination of selected optimal features. In the proposed method, tumor regions are extracted using the guidance information specified by the examiners, then morphological and texture features of tumor regions obtained from B-mode and CEUS images and TIC features obtained from CEUS video are extracted. Then, our method uses SVM classifiers to classify breast tumors as benign or malignant. During SVM training, many features are prepared, and useful features are selected. We name our proposed method \"Ceucia-Breast\" (Contrast Enhanced UltraSound Image Analysis for BREAST lesions).
The experimental results on 119 subjects show that the area under the receiver operating curve, accuracy, precision, and recall are 0.893, 0.816, 0.841 and 0.920, respectively. The classification performance is improved by our method over conventional methods using only B-mode images. In addition, we confirm that the selected features are consistent with the CEUS guidelines for breast tumor diagnosis. Furthermore, we conduct an experiment on the operator dependency of specifying guidance information and find that the intra-operator and inter-operator kappa coefficients are 1.0 and 0.798, respectively.
The experimental results show a significant improvement in classification performance compared to conventional classification methods using only B-mode images. We also confirm that the selected features are related to the findings that are considered important in clinical practice. Furthermore, we verify the intra- and inter-examiner correlation in the guidance input for region extraction and confirm that both correlations are in strong agreement.
The proposed method extracts spatial and temporal features from CEUS videos and breast tumors are classified as benign or malignant using linear support vector machines (SVM) with combination of selected optimal features. In the proposed method, tumor regions are extracted using the guidance information specified by the examiners, then morphological and texture features of tumor regions obtained from B-mode and CEUS images and TIC features obtained from CEUS video are extracted. Then, our method uses SVM classifiers to classify breast tumors as benign or malignant. During SVM training, many features are prepared, and useful features are selected. We name our proposed method \"Ceucia-Breast\" (Contrast Enhanced UltraSound Image Analysis for BREAST lesions).
The experimental results on 119 subjects show that the area under the receiver operating curve, accuracy, precision, and recall are 0.893, 0.816, 0.841 and 0.920, respectively. The classification performance is improved by our method over conventional methods using only B-mode images. In addition, we confirm that the selected features are consistent with the CEUS guidelines for breast tumor diagnosis. Furthermore, we conduct an experiment on the operator dependency of specifying guidance information and find that the intra-operator and inter-operator kappa coefficients are 1.0 and 0.798, respectively.
The experimental results show a significant improvement in classification performance compared to conventional classification methods using only B-mode images. We also confirm that the selected features are related to the findings that are considered important in clinical practice. Furthermore, we verify the intra- and inter-examiner correlation in the guidance input for region extraction and confirm that both correlations are in strong agreement.
摘要：
背景：近年来，超声造影（CEUS）已用于乳腺诊断的各种应用。超声造影在乳腺病变的超声诊断中相对于常规B超的优越性在临床实践中得到了广泛的证实。另一方面,在B型超声图像上计算机辅助诊断乳腺病变已经有很多建议，但对CEUS来说很少。提出一种基于机器学习的乳腺病变CEUS半自动分类方法。
方法：所提出的方法从CEUS视频中提取空间和时间特征，并使用线性支持向量机（SVM）结合选定的最佳特征将乳腺肿瘤分类为良性或恶性。在提出的方法中，使用检查者指定的指导信息来提取肿瘤区域，然后提取从B模式和CEUS图像获得的肿瘤区域的形态和纹理特征以及从CEUS视频获得的TIC特征。然后，我们的方法使用SVM分类器将乳腺肿瘤分类为良性或恶性.在SVM训练期间，准备了许多功能，并选择有用的功能。我们将我们提出的方法命名为“Ceucia-Breast”（胸部病变的对比增强超声图像分析）。
结果：对119名受试者的实验结果表明，受试者工作曲线下的面积，准确度,精度,和召回率分别为0.893、0.816、0.841和0.920。与仅使用B模式图像的常规方法相比，我们的方法提高了分类性能。此外,我们确认选定的特征符合CEUS乳腺肿瘤诊断指南.此外，我们对指定制导信息的算子依赖性进行了实验，发现算子内和算子间kappa系数分别为1.0和0.798。
结论：实验结果表明，与仅使用B模式图像的常规分类方法相比，分类性能有了显着提高。我们还确认所选择的特征与在临床实践中被认为重要的发现相关。此外，我们验证了区域提取指导输入中的检查者内和检查者间相关性,并确认这两种相关性非常一致.
方法：所提出的方法从CEUS视频中提取空间和时间特征，并使用线性支持向量机（SVM）结合选定的最佳特征将乳腺肿瘤分类为良性或恶性。在提出的方法中，使用检查者指定的指导信息来提取肿瘤区域，然后提取从B模式和CEUS图像获得的肿瘤区域的形态和纹理特征以及从CEUS视频获得的TIC特征。然后，我们的方法使用SVM分类器将乳腺肿瘤分类为良性或恶性.在SVM训练期间，准备了许多功能，并选择有用的功能。我们将我们提出的方法命名为“Ceucia-Breast”（胸部病变的对比增强超声图像分析）。
结果：对119名受试者的实验结果表明，受试者工作曲线下的面积，准确度,精度,和召回率分别为0.893、0.816、0.841和0.920。与仅使用B模式图像的常规方法相比，我们的方法提高了分类性能。此外,我们确认选定的特征符合CEUS乳腺肿瘤诊断指南.此外，我们对指定制导信息的算子依赖性进行了实验，发现算子内和算子间kappa系数分别为1.0和0.798。
结论：实验结果表明，与仅使用B模式图像的常规分类方法相比，分类性能有了显着提高。我们还确认所选择的特征与在临床实践中被认为重要的发现相关。此外，我们验证了区域提取指导输入中的检查者内和检查者间相关性,并确认这两种相关性非常一致.
