基於深度學習於2-D乳房超音波報告自動產生系統

林立

doi:10.6342/NTU201903872

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基於深度学习於2-D乳房超音波报告自动产生系统

Automatic Reporting System for 2-D Ultrasound Images Using Deep Learning

林立 , 硕士　　导师：张瑞峰　　

英文 DOI： 10.6342/NTU201903872

乳癌；医学影像报告； BI-RADS 字典；超音波影像； PSPNet ； Xception ；深度学习； breast cancer ； medical imaging reports ； BI-RADS lexicon ； ultrasound images ； PSPNet ； Xception ； deep learning

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摘要 │ 参考文献 (41) │ 国际计量

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Breast cancer is the most common cancer in women. Early detection could reduce the mortality rate of breast cancer. Ultrasound is often used for early detection, and ultrasound images are used to write the medical reports for the evaluation of further examinations by radiologists. However, for radiologists, writing reports requires domain knowledge of breast and skills of ultrasound images analysis, and it is tedious and time-consuming. In this study, an automatic reporting system was proposed to assist radiologists in writing the reports and analyzing the ultrasound images. First, the tumor region was extracted by the pyramid scene parsing network (PSPNet) segmentation model with dense dense condition random fields (CRFs). Second, the DL model and ML classifiers were applied to predict the lexicon of the tumor, and we also used the ensemble method the combine the DL model and ML classifiers. Finally, the predicted lexicons were applied to generate medical imaging reports. In this experiment, a totally of 318 tumors with ultrasound lexicons were used to evaluate our proposed method. According to the experiment results, the ensemble method using the average strategy combined with the DL model and the ML classifiers has the highest lexicon prediction performance, and the accuracy of lexicon prediction (shape, orientation, margin, heterogeneity, posterior features) were 85.85% (273/318), 83.02% (264/318), 80.19% (255/318), 78.62% (250/318), 87.11% (277/318).

参考文献 ( 41 ) 〈TOP〉

[1] F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal, "Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries," CA: A Cancer Journal for Clinicians, vol. 68, no. 6, pp. 394-424, Sep 2018.
[2] L. Wang, "Early Diagnosis of Breast Cancer," Sensors, vol. 17, no. 7, p. 1572, Jul 2017.
[3] E. B. Mendelson, M. Böhm-Vélez, and W. A. Berg, "ACR BI-RADS atlas, breast imaging reporting and data system.," Reston, VA: American College of Radiology, Jan 2014.
[4] P. Kisilev, E. Walach, E. Barkan, B. Ophir, S. Alpert, and S. Y. Hashoul, "From medical image to automatic medical report generation," IBM Journal of Research and Development, vol. 59, no. 2/3, pp. 2:1-2:7, Apr 2015.
[5] P. Kisilev, E. Walach, S. Y. Hashoul, E. Barkan, B. Ophir, and S. Alpert, "Semantic description of medical image findings: structured learning approach," in Proceedings of the British Machine Vision Conference (BMVC), 2015, pp. 171.1-171.11.

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使用DOI作为永久连结

引用含有DOI的文献

DOI可强化引用精确性、增强学术圈连结，并给予使用者跨平台的良好使用经验，目前在全世界已有超过五千万个对象申请DOI。 如想对DOI的使用与概念有进一步了解，请参考 （ ） 。

DOI可强化引用精确性、增强学术圈连结，并给予使用者跨平台的良好使用经验，目前在全世界已有超过五千万个对象申请DOI。如想对DOI的使用与概念有进一步了解，请参考（）。