The MOE Key Laboratory of Modern Acoustics, Department of Physics, Nanjing University, Nanjing, 210093, China.
Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
Sci Rep. 2020 Aug 6;10(1):13223. doi: 10.1038/s41598-020-70159-y.
This paper proposed a non-segmentation radiological method for classification of benign and malignant thyroid tumors using B mode ultrasound data. This method aimed to combine the advantages of morphological information provided by ultrasound and convolutional neural networks in automatic feature extraction and accurate classification. Compared with the traditional feature extraction method, this method directly extracted features from the data set without the need for segmentation and manual operations. 861 benign nodule images and 740 malignant nodule images were collected for training data. A deep convolution neural network VGG-16 was constructed to analyze test data including 100 malignant nodule images and 109 benign nodule images. A nine fold cross validation was performed for training and testing of the classifier. The results showed that the method had an accuracy of 86.12%, a sensitivity of 87%, and a specificity of 85.32%. This computer-aided method demonstrated comparable diagnostic performance with the result reported by an experienced radiologist based on American college of radiology thyroid imaging reporting and data system (ACR TI-RADS) (accuracy: 87.56%, sensitivity: 92%, and specificity: 83.49%). The automation advantage of this method suggested application potential in computer-aided diagnosis of thyroid cancer.
本文提出了一种基于 B 超数据的甲状腺良恶性肿瘤非分割放射学分类方法。该方法旨在结合超声提供的形态学信息和卷积神经网络在自动特征提取和准确分类方面的优势。与传统的特征提取方法相比，该方法直接从数据集提取特征，无需分割和人工操作。本研究共收集了 861 个良性结节图像和 740 个恶性结节图像作为训练数据。构建了深度卷积神经网络 VGG-16 来分析包括 100 个恶性结节图像和 109 个良性结节图像的测试数据。采用九折交叉验证方法对分类器进行训练和测试。结果表明，该方法的准确率为 86.12%，灵敏度为 87%，特异性为 85.32%。与基于美国放射学院甲状腺影像报告和数据系统（ACR TI-RADS）的有经验放射科医生报告的结果（准确率：87.56%，灵敏度：92%，特异性：83.49%）相比，该计算机辅助方法具有相当的诊断性能。该方法的自动化优势提示其在甲状腺癌计算机辅助诊断中有应用潜力。
