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首页> 外文期刊>Medical Imaging, IEEE Transactions on >Computer-Aided Lesion Diagnosis in Automated 3-D Breast Ultrasound Using Coronal Spiculation
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Computer-Aided Lesion Diagnosis in Automated 3-D Breast Ultrasound Using Coronal Spiculation

机译：使用冠状动脉造影在自动3D乳房超声检查中的计算机辅助病变诊断

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摘要

A computer-aided diagnosis (CAD) system for the classification of lesions as malignant or benign in automated 3-D breast ultrasound (ABUS) images, is presented. Lesions are automatically segmented when a seed point is provided, using dynamic programming in combination with a spiral scanning technique. A novel aspect of ABUS imaging is the presence of spiculation patterns in coronal planes perpendicular to the transducer. Spiculation patterns are characteristic for malignant lesions. Therefore, we compute spiculation features and combine them with features related to echotexture, echogenicity, shape, posterior acoustic behavior and margins. Classification experiments were performed using a support vector machine classifier and evaluation was done with leave-one-patient-out cross-validation. Receiver operator characteristic (ROC) analysis was used to determine performance of the system on a dataset of 201 lesions. We found that spiculation was among the most discriminative features. Using all features, the area under the ROC curve (Az) was 0.93, which was significantly higher than the performance without spiculation features (Az=0.90, p=0.02). On a subset of 88 cases, classification performance of CAD (Az=0.90) was comparable to the average performance of 10 readers (Az=0.87).
机译：提出了一种计算机辅助诊断（CAD）系统，用于在自动3-D乳房超声（ABUS）图像中将病变分类为恶性或良性。使用动态编程结合螺旋扫描技术，在提供种子点时自动对病变进行分割。 ABUS成像的一个新颖方面是在垂直于换能器的冠状平面中存在针刺图案。斑点状模式是恶性病变的特征。因此，我们计算针刺特征并将其与回声纹理，回声性，形状，后部声学行为和边界相关的特征结合起来。使用支持向量机分类器进行分类实验，并通过留一人交叉验证进行评估。接收者操作员特征（ROC）分析用于确定系统在201个病灶的数据集上的性能。我们发现针刺是最有区别的特征之一。使用所有特征，ROC曲线下的面积（A z ）为0.93，这明显高于不具有针刺特征的性能（A z = 0.90，p = 0.02 ）。在88例病例中，CAD的分类表现（A z = 0.90）与10位读者的平均表现（A z = 0.87）相当。

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