Automated Segmentation and Classification of Knee Synovitis Based on MRI Using Deep Learning

doi:10.1016/j.acra.2023.10.036

	Automated Segmentation and Classification of Knee Synovitis Based on MRI Using Deep Learning
	Wang, Qizheng 1; Yao, Meiyi 2; Song, Xinhang 2; Liu, Yandong 3; Xing, Xiaoying 1; Chen, Yongye 1; Zhao, Fangbo 4; Liu, Ke 1; Cheng, Xiaoguang 3; Jiang, Shuqiang 2; Lang, Ning 1
	2024-04-01
发表期刊	ACADEMIC RADIOLOGY
ISSN	1076-6332
卷号	31 期号:4 页码:1518-1527
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.
关键词	Knee Synovitis Magnetic resonance imaging Deep learning Diagnosis
DOI	10.1016/j.acra.2023.10.036
收录类别	SCI
语种	英语
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
WOS类目	Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001228506000001
出版者	ELSEVIER SCIENCE INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40071
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.