A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework

doi:10.1016/j.patter.2023.100826

	A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework
	Zhang, Jiadong1 ; Cui, Zhiming1 ; Shi, Zhenwei 2; Jiang, Yingjia 3; Zhang, Zhiliang 4; Dai, Xiaoting 5; Yang, Zhenlu 6; Gu, Yuning1 ; Zhou, Lei 7; Han, Chu 2; Huang, Xiaomei 8; Ke, Chenglu 2; Li, Suyun 2; Xu, Zeyan 2; Gao, Fei1 ; Zhou, Luping 9; Wang, Rongpin 6; Liu, Jun 3; Zhang, Jiayin 5; Ding, Zhongxiang 10; Sun, Kun 11; Li, Zhenhui 12; Liu, Zaiyi 2; Shen, Dinggang1,13,14
	2023-09-08
发表期刊	PATTERNS (IF:6.7[JCR-2023],6.6[5-Year])
ISSN	2666-3899
EISSN	2666-3899
卷号	4 期号:9
发表状态	已发表
DOI	10.1016/j.patter.2023.100826
摘要	Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows screening, follow up, and diagnosis for breast tumor with high sensitivity. Accurate tumor segmentation from DCE-MRI can provide crucial information of tumor location and shape, which significantly influences the downstream clinical decisions. In this paper, we aim to develop an artificial intelligence (AI) assistant to automatically segment breast tumors by capturing dynamic changes in multi-phase DCE-MRI with a spatial-temporal framework. The main advantages of our AI assistant include (1) robustness, i.e., our model can handle MR data with different phase numbers and imaging intervals, as demonstrated on a large-scale dataset from seven medical centers, and (2) efficiency, i.e., our AI assistant significantly reduces the time required for manual annotation by a factor of 20, while maintaining accuracy comparable to that of physicians. More importantly, as the fundamental step to build an AI-assisted breast cancer diagnosis system, our AI assistant will promote the application of AI in more clinical diagnostic practices regarding breast cancer. © 2023 The Authors
关键词	Diagnosis Large dataset Magnetic resonance imaging Medical imaging Tumors Breast tumour Domain problems DSML 3: development/pre-production: data science output have been rolled out/validated across multiple domain/problem Dynamic contrast-enhanced magnetic resonance imaging Follow up Multiple domains Pre-production Production data Spatial temporals Tumor segmentation
收录类别	EI
语种	英语
出版者	Cell Press
EI入藏号	20233614690570
EI主题词	Diseases
EI分类号	461.1 Biomedical Engineering ; 461.2 Biological Materials and Tissue Engineering ; 461.6 Medicine and Pharmacology ; 701.2 Magnetism: Basic Concepts and Phenomena ; 723.2 Data Processing and Image Processing ; 746 Imaging Techniques
原始文献类型	Journal article (JA)
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/329006
专题	生物医学工程学院信息科学与技术学院_PI研究组_高飞组信息科学与技术学院_硕士生生物医学工程学院_PI研究组_沈定刚组生物医学工程学院_PI研究组_崔智铭组
通讯作者	Wang, Rongpin; Liu, Jun; Zhang, Jiayin; Ding, Zhongxiang; Sun, Kun; Li, Zhenhui; Liu, Zaiyi; Shen, Dinggang
作者单位	1.School of Biomedical Engineering, ShanghaiTech University, Shanghai; 201210, China; 2.Department of Radiology, Guangdong Provincial People's Hospital, Guangdong; 510080, China; 3.Department of Radiology, The Second Xiangya Hospital, Central South University, Hunan; 410011, China; 4.School of Medical Imaging, Hangzhou Medical College, Zhejiang; 310059, China; 5.Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai; 200080, China; 6.Department of Radiology, Guizhou Provincial People's Hospital, Guizhou; 550002, China; 7.School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai; 200093, China; 8.Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou; 510515, China; 9.School of Electrical and Information Engineering, The University of Sydney, Sydney; NSW; 2006, Australia; 10.Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Hangzhou; 310003, China; 11.Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai; 200025, China; 12.Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming; 650118, China; 13.Shanghai United Imaging Intelligence Co., Ltd., Shanghai; 200230, China; 14.Shanghai Clinical Research and Trial Center, Shanghai; 200052, China
第一作者单位	生物医学工程学院
通讯作者单位	生物医学工程学院
第一作者的第一单位	生物医学工程学院
推荐引用方式 GB/T 7714	Zhang, Jiadong,Cui, Zhiming,Shi, Zhenwei,et al. A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework[J]. PATTERNS,2023,4(9).
APA	Zhang, Jiadong.,Cui, Zhiming.,Shi, Zhenwei.,Jiang, Yingjia.,Zhang, Zhiliang.,...&Shen, Dinggang.(2023).A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework.PATTERNS,4(9).
MLA	Zhang, Jiadong,et al."A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework".PATTERNS 4.9(2023).