中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Automated Segmentation and Classification of Knee Synovitis Based on MRI Using Deep Learning

文献类型：期刊论文

作者Wang, Qizheng1; Yao, Meiyi2; Song, Xinhang2; Liu, Yandong3; Xing, Xiaoying1; Chen, Yongye1; Zhao, Fangbo4; Liu, Ke1; Cheng, Xiaoguang3; Jiang, Shuqiang2
刊名ACADEMIC RADIOLOGY
出版日期2024-04-01
卷号31期号:4页码:1518-1527
关键词Knee Synovitis Magnetic resonance imaging Deep learning Diagnosis
ISSN号1076-6332
DOI10.1016/j.acra.2023.10.036
英文摘要Objectives: To develop a deep learning (DL) model for segmentation of the suprapatellar capsule (SC) and infrapatellar fat pad (IPFP) based on sagittal proton density-weighted images and to distinguish between three common types of knee synovitis. Materials and Methods: This retrospective study included 376 consecutive patients with pathologically confirmed knee synovitis (rheumatoid arthritis, gouty arthritis, and pigmented villonodular synovitis) from two institutions. A semantic segmentation model was trained on manually annotated sagittal proton density-weighted images. The segmentation results of the regions of interest and patients' sex and age were used to classify knee synovitis after feature processing. Classification by the DL method was compared to the classification performed by radiologists. Results: Data of the 376 patients (mean age, 42 +/- 15 years; 216 men) were separated into a training set ( n = 233), an internal test set ( n = 93), and an external test set ( n = 50). The automated segmentation model showed good performance (mean accuracy: 0.99 and 0.99 in the internal and external test sets). On the internal test set, the DL model performed better than the senior radiologist (accuracy: 0.86 vs. 0.79; area under the curve [AUC]: 0.83 vs. 0.79). On the external test set, the DL diagnostic model based on automatic segmentation performed as well or better than senior and junior radiologists (accuracy: 0.79 vs. 0.79 vs. 0.73; AUC: 0.76 vs. 0.77 vs. 0.70). Conclusion: DL models for segmentation of SC and IPFD can accurately classify knee synovitis and aid radiologic diagnosis.
资助项目Beijing Natural Science Foundation[Z190020] ; National Natural Science Foundation of China[81971578] ; National Natural Science Foundation of China[62032022] ; National Natural Science Foundation of China[61902378]
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:001228506000001
出版者ELSEVIER SCIENCE INC
源URL[http://119.78.100.204/handle/2XEOYT63/40070]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Lang, Ning
作者单位1.Peking Univ Third Hosp, Dept Radiol, 49 North Garden Rd,Haidian Dist, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
3.Beijing Jishuitan Hosp, Dept Radiol, 31 Xinjiekou East St, Beijing, Peoples R China
4.Peking Univ, 5 Yiheyuan Rd Haidian Dist, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Qizheng,Yao, Meiyi,Song, Xinhang,et al. Automated Segmentation and Classification of Knee Synovitis Based on MRI Using Deep Learning[J]. ACADEMIC RADIOLOGY,2024,31(4):1518-1527.
APA Wang, Qizheng.,Yao, Meiyi.,Song, Xinhang.,Liu, Yandong.,Xing, Xiaoying.,...&Lang, Ning.(2024).Automated Segmentation and Classification of Knee Synovitis Based on MRI Using Deep Learning.ACADEMIC RADIOLOGY,31(4),1518-1527.
MLA Wang, Qizheng,et al."Automated Segmentation and Classification of Knee Synovitis Based on MRI Using Deep Learning".ACADEMIC RADIOLOGY 31.4(2024):1518-1527.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。