Evaluation of a deep learning-based computer-aided diagnosis system for distinguishing benign from malignant thyroid nodules in ultrasound images
Evaluation of a deep learning-based computer-aided diagnosis system for distinguishing benign from malignant thyroid nodules in ultrasound images
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DOI：
10.1002/mp.14301
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发表时间：
2020-06-25
期刊：
影响因子：
3.8
通讯作者：
Niu, Lijuan
中科院分区：
文献类型：
--
作者：
Sun, Chao;Zhan, Yukang;Niu, Lijuan
目的计算机辅助诊断(CAD)系统帮助解决通常依赖个人经验的主观诊断问题。基于深度学习方法，开发了一种用于区分超声图像中甲状腺结节良恶性的计算机辅助设计系统。比较了CAD系统与经验丰富的主治医师的诊断性能。方法训练CAD系统的超声图像数据集包括651个恶性结节和386个良性结节，用于测试的数据库包括422个恶性结节和128个良性结节。所有结节均经病理证实。在该CAD系统中，利用支持向量机进行分类，并将卷积神经网络提取的深层特征与人工构造的方向梯度直方图(HOG)、局部二值模式(LBP)和尺度不变特征(SIFT)相融合。根据最大类分离距离选取融合后的特征构成最优特征子集，作为支持向量机的训练样本。结果该系统的准确性、敏感性和特异度分别为92.5%、96.4%和83.1%，均高于有经验的主治医师。计算机辅助设计系统和主治医师的ROC曲线下面积分别为0.881和0.819。结论与经验丰富的主治医师相比，该系统对甲状腺结节的诊断效果更好。该系统可作为超声诊断甲状腺结节的可靠辅助工具。超声图像中的宏观特征，如甲状腺结节的边缘和形状，可能会影响CAD系统的诊断效率。
Purpose Computer-aided diagnosis (CAD) systems assist in solving subjective diagnosis problems that typically rely on personal experience. A CAD system has been developed to differentiate malignant thyroid nodules from benign thyroid nodules in ultrasound images based on deep learning methods. The diagnostic performance was compared between the CAD system and the experienced attending radiologists. Methods The ultrasound image dataset for training the CAD system included 651 malignant nodules and 386 benign nodules while the database for testing included 422 malignant nodules and 128 benign nodules. All the nodules were confirmed by pathology results. In the proposed CAD system, a support vector machine (SVM) is used for classification and fused features which combined the deep features extracted by a convolutional neural network (CNN) with the hand-crafted features such as the histogram of oriented gradient (HOG), local binary patterns (LBP), and scale invariant feature transform (SIFT) were obtained. The optimal feature subset was formed by selecting these fused features based on the maximum class separation distance and used as the training sample for the SVM. Results The accuracy, sensitivity, and specificity of the CAD system were 92.5%, 96.4%, and 83.1%, respectively, which were higher than those of the experienced attending radiologists. The areas under the ROC curves of the CAD system and the attending radiologists were 0.881 and 0.819, respectively. Conclusions The CAD system for thyroid nodules exhibited a better diagnostic performance than experienced attending radiologists. The CAD system could be a reliable supplementary tool to diagnose thyroid nodules using ultrasonography. Macroscopic features in ultrasound images, such as the margins and shape of thyroid nodules, could influence the diagnostic efficiency of the CAD system.