Department of Diagnostic Radiology, Seoul National University Hospital, Korea.
Ultrasound Med Biol. 2011 Apr;37(4):539-48. doi: 10.1016/j.ultrasmedbio.2011.01.006.
New automated whole breast ultrasound (ABUS) machines have recently been developed and the ultrasound (US) volume dataset of the whole breast can be acquired in a standard manner. The purpose of this study was to develop a novel computer-aided diagnosis system for classification of breast masses in ABUS images. One hundred forty-seven cases (76 benign and 71 malignant breast masses) were obtained by a commercially available ABUS system. Because the distance of neighboring slices in ABUS images is fixed and small, these continuous slices were used for reconstruction as three-dimensional (3-D) US images. The 3-D tumor contour was segmented using the level-set segmentation method. Then, the 3-D features, including the texture, shape and ellipsoid fitting were extracted based on the segmented 3-D tumor contour to classify benign and malignant tumors based on the logistic regression model. The Student's t test, Mann-Whitney U test and receiver operating characteristic (ROC) curve analysis were used for statistical analysis. From the Az values of ROC curves, the shape features (0.9138) are better than the texture features (0.8603) and the ellipsoid fitting features (0.8496) for classification. The difference was significant between shape and ellipsoid fitting features (p = 0.0382). However, combination of ellipsoid fitting features and shape features can achieve a best performance with accuracy of 85.0% (125/147), sensitivity of 84.5% (60/71), specificity of 85.5% (65/76) and the area under the ROC curve Az of 0.9466. The results showed that ABUS images could be used for computer-aided feature extraction and classification of breast tumors.
新的全自动全乳房超声（ABUS）仪器最近已经开发出来，整个乳房的超声（US）体积数据集可以以标准的方式获得。本研究的目的是开发一种新的计算机辅助诊断系统，用于分类 ABUS 图像中的乳腺肿块。通过市售的 ABUS 系统获得了 147 例（76 例良性和 71 例恶性乳腺肿块）。由于 ABUS 图像中相邻切片的距离固定且较小，因此这些连续切片被用于重建为三维（3-D）US 图像。使用水平集分割方法对 3-D 肿瘤轮廓进行分割。然后，基于分割的 3-D 肿瘤轮廓提取 3-D 特征，包括纹理、形状和椭圆拟合，以基于逻辑回归模型对良性和恶性肿瘤进行分类。使用学生 t 检验、Mann-Whitney U 检验和接收者操作特征（ROC）曲线分析进行统计分析。从 ROC 曲线的 Az 值可以看出，形状特征（0.9138）比纹理特征（0.8603）和椭圆拟合特征（0.8496）更适合分类。形状特征和椭圆拟合特征之间的差异具有统计学意义（p=0.0382）。然而，结合椭圆拟合特征和形状特征可以实现最佳性能，准确率为 85.0%（125/147），敏感度为 84.5%（60/71），特异性为 85.5%（65/76），ROC 曲线下面积 Az 为 0.9466。结果表明，ABUS 图像可用于乳腺肿瘤的计算机辅助特征提取和分类。
