Journal of Software:2020.31(7):2245-2282

(西南交通大学 信息科学与技术学院, 四川 成都 610031;四川省医学科学院·四川省人民医院, 四川 成都 610072)
Survey of Automatic Ultrasonographic Analysis for Thyroid and Breast
GONG Xun,YANG Fei,DU Zhang-Jin,SHI En,ZHAO Xu,YANG Zi-Qi,ZOU Hai-Peng,LUO Jun
(School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China;Sichuan Academy of Medical Sciences · Sichuan Provincial People's Hospital, Chengdu 610072, China)
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Received:August 04, 2019    Revised:September 19, 2019
> 中文摘要: 超声诊断是甲状腺、乳腺癌首选影像学检查和术前评估方法.但良/恶性结节的超声表现存在重叠,仍欠缺定量、稳定的分析手段,严重依赖操作者的经验.近年来,基于计算机技术的医疗影像分析水平快速发展,超声影像分析取得了一系列里程碑式的突破,为医疗提供有效的诊断决策支持.以甲状腺、乳腺两类超声影像为对象,梳理了计算机视觉、图像识别技术在医学超声图像上的学术进展,以超声影像自动诊断涉及的一系列关键技术为主线,从图像预处理、病灶区定位及分割、特征提取和分类这4个方面对近年来主流算法进行了详尽的综述分析,从算法分析、数据和评估方法等方面进行多维度梳理.最后讨论了具体面向这两种腺体的超声图像计算机分析存在的问题,并对此领域的研究趋势和发展方向进行了展望.
Abstract:Ultrasonography is the first choice of imaging examination and preoperative evaluation for thyroid and breast cancer. However, ultrasonic characteristics of benign and malignant nodules are commonly overlapped. The diagnosis heavily relies on operator's experience other than quantitative and stable methods. In recent years, medical imaging analysis based on computer technology has developed rapidly, and a series of landmark breakthroughs have been made, which provides effective decision supports for medical imaging diagnosis. In this work, the research progress of computer vision and image recognition technologies in thyroid and breast ultrasound images is studied. A series of key technologies involved in automatic diagnosis of ultrasound images is the main lines of the work. The major algorithms in recent years are summarized and analyzed, such as ultrasound image preprocessing, lesion localization and segmentation, feature extraction and classification. Moreover, multi-dimensional analysis is made on the algorithms, data sets, and evaluation methods. Finally, existing problems related to automatic analysis of those two kinds of ultrasound imaging are discussed, research trend and development direction in the field of ultrasound images analysis are discussed.
文章编号:     中图分类号:TP391    文献标志码:
基金项目:国家自然科学基金(61876158);四川省重点研发项目(2019YFS0432) 国家自然科学基金(61876158);四川省重点研发项目(2019YFS0432)
Foundation items:National Natural Science Foundation of China (61876158); Sichuan Science and Technology Program (2019YFS0432)
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GONG Xun,YANG Fei,DU Zhang-Jin,SHI En,ZHAO Xu,YANG Zi-Qi,ZOU Hai-Peng,LUO Jun.Survey of Automatic Ultrasonographic Analysis for Thyroid and Breast.Journal of Software,2020,31(7):2245-2282