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首页> 外文会议>International Workshop on Breast Imaging >Computerized Classification Scheme for Distinguishing between Benign and Malignant Masses by Analyzing Multiple MRI Sequences with Convolutional Neural Network
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Computerized Classification Scheme for Distinguishing between Benign and Malignant Masses by Analyzing Multiple MRI Sequences with Convolutional Neural Network

机译：通过卷积神经网络分析多个MRI序列来区分良恶性的计算机分类方案

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Breast magnetic resonance imaging (MRI) has a higher sensitivity of early breast cancer than mammography, but the specificity is lower. In MRI examination at clinical practice, multiple MRI sequences are usually acquired to achieve high diagnostic accuracy. The purpose of this study was to develop a computerized classification scheme for distinguishing between benign and malignant masses by integrally analyzing multiple MRI sequences with convolutional neural networks (CNNs). Our database consisted of four MRI sequences for 43 patients with masses. It included T1-weighted images, T2-weighted images, dynamic contrast material-enhanced magnetic resonance imaging (DCE-MRI) images, and the difference images of the DCE-MRI images for each patient. In training the CNNs, the CNNs were first trained independently for each MRI sequence. The CNN features extracted from four MRI sequences with the trained CNNs were then inputted to a support vector machine (SVM) for distinguishing between benign and malignant masses. A κ-fold cross validation method (κ=3) was used for training and testing the CNNs and the SVM. With the proposed method, the classification accuracy, the sensitivity, the specificity, the positive predictive value, and the negative predictive value were 88.4% (38/43), 90.0% (27/30), 84.6% (11/13), 78.6% (11/14), and 93.1% (27/29), respectively. The classification performance with the proposed method analyzing multiple MRI sequences was substantially greater than those with CNNs analyzing one MRI sequence. The proposed method achieved high classification performance and would be useful in differential diagnoses of masses as diagnostic aid.
机译：乳腺磁共振成像（MRI）对早期乳腺癌的敏感性高于乳腺X线摄影，但特异性较低。在临床实践中的MRI检查中，通常需要获取多个MRI序列以实现较高的诊断准确性。这项研究的目的是通过使用卷积神经网络（CNN）对多个MRI序列进行整体分析，从而开发出一种区分良性和恶性肿块的计算机分类方案。我们的数据库包含43例肿块患者的四个MRI序列。它包括每个患者的T1加权图像，T2加权图像，动态对比材料增强磁共振成像（DCE-MRI）图像和DCE-MRI图像的差异图像。在训练CNN时，首先针对每个MRI序列分别对CNN进行训练。然后将从具有训练后的CNN的四个MRI序列中提取的CNN特征输入到支持向量机（SVM），以区分良性和恶性肿块。使用κ倍交叉验证方法（κ= 3）来训练和测试CNN和SVM。利用该方法，分类准确性，敏感性，特异性，阳性预测值和阴性预测值分别为88.4％（38/43），90.0％（27/30），84.6％（11/13），分别为78.6％（11/14）和93.1％（27/29）。所提出的分析多个MRI序列的方法的分类性能显着高于CNN分析一个MRI序列的方法。所提出的方法具有很高的分类性能，可作为诊断辅助手段用于肿块的鉴别诊断。

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