基于迁移学习的乳腺肿瘤超声图像智能分类诊断
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摘要
目的探讨迁移学习方法对乳腺良恶性肿瘤超声图像分类的价值。方法回顾性分析经病理证实的447例乳腺肿瘤的超声声像图,采用主成分分析法对原始图像进行分析提取;在Matlab 7.0软件中编程实现迁移学习,将量化的图像特征作为输入数据,利用迁移学习对乳腺良恶性肿瘤进行智能分类。结果乳腺恶性肿瘤的边缘粗糙度、坚固度、邻域灰度差矩阵粗糙度、肿瘤后方与周围区域回声差异及水平方向高频分量和垂直方向低频分量的直方图能量均明显高于良性肿瘤(P均<0.05)。超声和迁移学习方法诊断乳腺恶性肿瘤的敏感度分别为96.21%(127/132)和96.04%(97/101),特异度为66.35%(209/315)和98.49%(196/199),准确率为75.17%(336/447)和97.67%(293/300)。结论超声图像特征定量化可为识别良恶性乳腺肿瘤提供客观的量化参数;迁移学习可有效对乳腺良恶性肿瘤的声像图进行分类。
Objective To investigate the value of transfer learning methods in classification of ultrasound images of benign and malignant breast tumors.Methods Ultrasonic features of histopathologically proved breast tumors in 447 patients were retrospectively analyzed.The features of original images were extracted using the method of principal component analysis.Matlab 7.0 software was used for achieving transfer learning method.Finally,the quantitative image characteristics were inputted into the program in order to use new methods of transfer learning for identifying the benign and malignant breast tumors.Results The quantitative parameters of ultrasound images with malignant breast tumors,such as edge roughness,firmness,neighborhood gray-tone difference matrix roughness,echo difference between the posterior and peripheral areas of the masses,and the horizontal high-frequency and vertical low-frequency componentshistogram energy were significantly higher than those of the benign breast tumors(all P <0.05).The sensitivity,specificity,the accuracy of the ultrasound and transfer learning method in diagnosis of malignant breast tumors was96.21%(127/132)and 96.04%(97/101),66.35%(209/315)and 98.49%(196/199),75.17%(336/447)and 97.67%(293/300),respectively.Conclusion Quantitative ultrasonic features can provide objective quantitative parameters for identification of benign and malignant breast tumors.Transfer learning methods can effectively classify ultrasound images with benign and malignant breast tumors.
Objective To investigate the value of transfer learning methods in classification of ultrasound images of benign and malignant breast tumors.Methods Ultrasonic features of histopathologically proved breast tumors in 447 patients were retrospectively analyzed.The features of original images were extracted using the method of principal component analysis.Matlab 7.0 software was used for achieving transfer learning method.Finally,the quantitative image characteristics were inputted into the program in order to use new methods of transfer learning for identifying the benign and malignant breast tumors.Results The quantitative parameters of ultrasound images with malignant breast tumors,such as edge roughness,firmness,neighborhood gray-tone difference matrix roughness,echo difference between the posterior and peripheral areas of the masses,and the horizontal high-frequency and vertical low-frequency componentshistogram energy were significantly higher than those of the benign breast tumors(all P <0.05).The sensitivity,specificity,the accuracy of the ultrasound and transfer learning method in diagnosis of malignant breast tumors was96.21%(127/132)and 96.04%(97/101),66.35%(209/315)and 98.49%(196/199),75.17%(336/447)and 97.67%(293/300),respectively.Conclusion Quantitative ultrasonic features can provide objective quantitative parameters for identification of benign and malignant breast tumors.Transfer learning methods can effectively classify ultrasound images with benign and malignant breast tumors.
引文
[1] 肖晓云,董理聪,吴欢,等.BIRADS联合CEUS鉴别诊断乳腺肿瘤的良恶性.中国医学影像技术,2016,32(6):896-899.
[2] Singh BK, Verma K, Thoke AS. Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images. Expert Syst Appl, 2016,66:114-123.
[3] 张松松,张玉梅,尹逊娣,等.超声弹性成像定量分析诊断BI-RADS 4类乳腺肿块良恶性.中国医学影像技术,2016,32(7):1065-1069.
[4] Wu WJ, Lin SW, Moon WK. An artificial immune system-based support vector machine approach for classifying ultrasound breast tumor images. J Digit Imaging, 2015,28(5):576-585.
[5] 沈嘉琳.乳腺肿瘤的超声图像分析及良恶性判别.上海:复旦大学,2006:9-80.
[6] Ardakani AA, Gharbali A, Mohammadi A. Classification of breast tumors using sonographic texture analysis. J Ultrasound Med, 2015,34(2):225-231.
[7] Moon WK, Shen YW, Huang CS, et al. Computer-aided diagnosis for the classification of breast masses in automated whole breast ultrasound images. Ultrasound Med Biol, 2011,37(4):539-548.
[8] Oelze ML, Mamou J. Review of quantitative ultrasound: Envelope statistics and backscatter coefficient imaging and contributions to diagnostic ultrasound. IEEE Trans Ultrason Ferroelectr Freq Control, 2016,63(2):336-351.
[9] Huang YL, Chen DR, Jiang YR, et al. Computer-aided diagnosis using morphological features for classifying breast lesions on ultrasound. Ultrasound Obstet Gynecol, 2010,32(4):565-572.
[10] Heimann T, Mountney P, John M, et al. Real-time ultrasound transducer localization in fluoroscopy images by transfer learning from synthetic training data. Med Image Anal, 2014,18(8):1320-1328.
[11] Pan SJ, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng, 2010,22(10):1345-1359.
[12] 庄福振,罗平,何清,等.迁移学习研究进展.软件学报,2015,26(1):26-39.
[13] Guyon I, Dror G, Lemaire V, et al. Unsupervised and transfer learning challenge//IEEE. International Joint Conference on Neural Networks. San Jose: IEEE, 2011:793-800.
[14] Zhu Y, Chen Y, Lu Z, et al.Heterogeneous transfer learning for image classification//AAAI. AAAI'11 Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence. San Francisco: AAAI, 2011:1304-1309.
[15] 邵忻.基于跨领域主动学习的图像分类方法.计算机应用,2014,34(4):1169-1171.
[16] Stavros AT. Breast Ultrasound. [s.l.]: Lippincott Williams & Wilkins, 2004:332-500.
[17] 李佳伟,时兆婷,郭翌,等.超声影像组学对浸润性乳腺癌激素受体表达预测价值的探索性研究.肿瘤影像学,2017,26(2):128-135.
[18] Nascimento CDL, Silva SDDS, Silva TAD, et al. Breast tumor classification in ultrasound images using support vector machines and neural networks. Research on Biomedical Engineering, 2016,32(3):283-292.
[19] Zhang Q, Xiao Y, Dai W, et al. Deep learning based classification of breast tumors with shear-wave elastography. Ultrasonics, 2016,72:150-157.
[2] Singh BK, Verma K, Thoke AS. Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images. Expert Syst Appl, 2016,66:114-123.
[3] 张松松,张玉梅,尹逊娣,等.超声弹性成像定量分析诊断BI-RADS 4类乳腺肿块良恶性.中国医学影像技术,2016,32(7):1065-1069.
[4] Wu WJ, Lin SW, Moon WK. An artificial immune system-based support vector machine approach for classifying ultrasound breast tumor images. J Digit Imaging, 2015,28(5):576-585.
[5] 沈嘉琳.乳腺肿瘤的超声图像分析及良恶性判别.上海:复旦大学,2006:9-80.
[6] Ardakani AA, Gharbali A, Mohammadi A. Classification of breast tumors using sonographic texture analysis. J Ultrasound Med, 2015,34(2):225-231.
[7] Moon WK, Shen YW, Huang CS, et al. Computer-aided diagnosis for the classification of breast masses in automated whole breast ultrasound images. Ultrasound Med Biol, 2011,37(4):539-548.
[8] Oelze ML, Mamou J. Review of quantitative ultrasound: Envelope statistics and backscatter coefficient imaging and contributions to diagnostic ultrasound. IEEE Trans Ultrason Ferroelectr Freq Control, 2016,63(2):336-351.
[9] Huang YL, Chen DR, Jiang YR, et al. Computer-aided diagnosis using morphological features for classifying breast lesions on ultrasound. Ultrasound Obstet Gynecol, 2010,32(4):565-572.
[10] Heimann T, Mountney P, John M, et al. Real-time ultrasound transducer localization in fluoroscopy images by transfer learning from synthetic training data. Med Image Anal, 2014,18(8):1320-1328.
[11] Pan SJ, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng, 2010,22(10):1345-1359.
[12] 庄福振,罗平,何清,等.迁移学习研究进展.软件学报,2015,26(1):26-39.
[13] Guyon I, Dror G, Lemaire V, et al. Unsupervised and transfer learning challenge//IEEE. International Joint Conference on Neural Networks. San Jose: IEEE, 2011:793-800.
[14] Zhu Y, Chen Y, Lu Z, et al.Heterogeneous transfer learning for image classification//AAAI. AAAI'11 Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence. San Francisco: AAAI, 2011:1304-1309.
[15] 邵忻.基于跨领域主动学习的图像分类方法.计算机应用,2014,34(4):1169-1171.
[16] Stavros AT. Breast Ultrasound. [s.l.]: Lippincott Williams & Wilkins, 2004:332-500.
[17] 李佳伟,时兆婷,郭翌,等.超声影像组学对浸润性乳腺癌激素受体表达预测价值的探索性研究.肿瘤影像学,2017,26(2):128-135.
[18] Nascimento CDL, Silva SDDS, Silva TAD, et al. Breast tumor classification in ultrasound images using support vector machines and neural networks. Research on Biomedical Engineering, 2016,32(3):283-292.
[19] Zhang Q, Xiao Y, Dai W, et al. Deep learning based classification of breast tumors with shear-wave elastography. Ultrasonics, 2016,72:150-157.
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