个人中心| | 帮助
高级检索
当前位置： 首页 > 详情页

Virtual elastography ultrasound via generative adversarial network for breast cancer diagnosis

| 认领 | 导出 | |

文献详情

资源类型：
WOS体系：
Pubmed体系：

收录情况： ◇ SCIE ◇ 自然指数

作者：
Yao Zhao[1] ; Luo Ting[2] ; Dong YiJie[2] ; Jia XiaoHong[2] ; Deng YinHui[1] ; Wu GuoQing[1] ; Zhu Ying[2] ; Zhang JingWen[2] ; Liu Juan[2] ; Yang LiChun[3] ; Luo XiaoMao[3] ; Li ZhiYao[3] ; Xu YanJun[4] ; Hu Bin[5] ; Huang YunXia[6] ; Chang Cai[6] ; Xu JinFeng[7] ; Luo Hui[7] ; Dong FaJin[7] ; Xia XiaoNa[8] ; Wu ChengRong[8] ; Hu WenJia[9] ; Wu Gang[9] ; Li QiaoYing[10] ; Chen Qin[11] ; Deng WanYue[11] ; Jiang QiongChao[12] ; Mou YongLin[13] ; Yan HuanNan[13] ; Xu XiaoJing[14] ; Yan HongJu[14] ; Zhou Ping[15] ; Shao Yang[15] ; Cui LiGang[16] ; He Ping[16] ; Qian LinXue[17] ; Liu JinPing[17] ; Shi LiYing[18] ; Zhao YaNan[19] ; Xu YongYuan[19] ; Zhan WeiWei[2] ; Wang YuanYuan[1] ; Yu JinHua[1] ; Zhou JianQiao[2] ;
机构： [1]School of Information Science and Technology, Fudan University, Shanghai,China. [2]Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China. [3]Department of Medical Ultrasound, Yunnan Cancer Hospital & The Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China. [4]Department of Ultrasound in Medicine, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai Institute of Ultrasound in Medicine, Shanghai 200233, China. [5]Department of Ultrasound, Minhang Hospital, Fudan University, Shanghai 201199, China. [6]Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai 200032, China. [7]Department of Ultrasound, Shenzhen People’sHospital, Guangzhou, Guangdong, China. [8]Department of Ultrasound Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China. [9]Department of Ultrasound, People’s Hospital of Henan Province, Zhengzhou 450000, China. [10]Department of Ultrasound Diseases, Tangdu Hospital, Four Military Medical University, Xi’an 710038, China. [11]Department of Ultrasound, Sichuan Provincial People’s Hospital,University of ElectronicScience and Technology ofChina,Chengdu 610072,China. [12]Department of Ultrasound, Sun Yat-senMemorialHospital, Sun Yat-sen University, Guangzhou 510120, China. [13]Department of Ultrasound, General Hospital of Northern Theater Command, 110000 Shenyang, China. [14]Department of Ultrasound, Affiliated Hangzhou First people’s Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China. [15]Department of Ultrasound, The Third Xiangya Hospital of Central South University, Changsha 410013, China. [16]Department of Ultrasound, Peking University Third Hospital, Beijing 100191,China. [17]Department of Ultrasound, Beijing Friendship Hospital,Capital MedicalUniversity, Beijing 100050, China. [18]Department ofUltrasound, Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou 550004, China. [19]Department of Ultrasound, Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou 310009, China.
出处：
DOI： 10.1038/s41467-023-36102-1
ISSN：

摘要：
Elastography ultrasound (EUS) imaging is a vital ultrasound imaging modality. The current use of EUS faces many challenges, such as vulnerability to subjective manipulation, echo signal attenuation, and unknown risks of elastic pressure in certain delicate tissues. The hardware requirement of EUS also hinders the trend of miniaturization of ultrasound equipment. Here we show a cost-efficient solution by designing a deep neural network to synthesize virtual EUS (V-EUS) from conventional B-mode images. A total of 4580 breast tumor cases were collected from 15 medical centers, including a main cohort with 2501 cases for model establishment, an external dataset with 1730 cases and a portable dataset with 349 cases for testing. In the task of differentiating benign and malignant breast tumors, there is no significant difference between V-EUS and real EUS on high-end ultrasound, while the diagnostic performance of pocket-sized ultrasound can be improved by about 5% after V-EUS is equipped.© 2023. The Author(s).

基金：
This study was supported by the National Natural Science Foundation of China under Grants 91959127 (JHY) and 82071928 (JQZ) and Shanghai science and technology action innovation plan under Grants 19441903100 (JHY).
语种：
外文
被引次数：
WOS：
PubmedID：
中科院(CAS)分区：
出版当年[2023]版：
大类 | 1 区 综合性期刊
小类 | 1 区 综合性期刊
最新[2023]版：
大类 | 1 区 综合性期刊
小类 | 1 区 综合性期刊
JCR分区：
出版当年[2023]版：
Q1 MULTIDISCIPLINARY SCIENCES
最新[2023]版：
Q1 MULTIDISCIPLINARY SCIENCES

影响因子： 最新[2023版] 最新五年平均 出版当年[2023版] 出版当年五年平均 出版前一年[2022版]

第一作者：
第一作者机构： [1]School of Information Science and Technology, Fudan University, Shanghai,China.
共同第一作者：
通讯作者：
推荐引用方式(GB/T 7714)：
Yao Zhao,Luo Ting,Dong YiJie,et al.Virtual elastography ultrasound via generative adversarial network for breast cancer diagnosis[J].NATURE COMMUNICATIONS.2023,14(1):doi:10.1038/s41467-023-36102-1.
APA：
Yao Zhao,Luo Ting,Dong YiJie,Jia XiaoHong,Deng YinHui...&Zhou JianQiao.(2023).Virtual elastography ultrasound via generative adversarial network for breast cancer diagnosis.NATURE COMMUNICATIONS,14,(1)
MLA：
Yao Zhao,et al."Virtual elastography ultrasound via generative adversarial network for breast cancer diagnosis".NATURE COMMUNICATIONS 14..1(2023)

资源点击量：43377 今日访问量：0 总访问量：3120 更新日期：2024-09-01 建议使用谷歌、火狐浏览器 常见问题

版权所有©2020 四川省肿瘤医院 技术支持：重庆聚合科技有限公司 地址：成都市人民南路四段55号