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Assessing abnormal corneal endothelial cells from in vivo confocal microscopy images using a fully automated deep learning system

文献类型：期刊论文

作者Qu, Jinghao1,2; Qin, Xiaoran3; Peng, Rongmei1,2; Xiao, Gege1,2; Gu, Shaofeng1,2; Wang, Haikun1,2; Hong, Jing1,2
刊名EYE AND VISION
出版日期2023-06-01
卷号10期号:1页码:10
ISSN号2326-0254
关键词Abnormal corneal endothelial cells LASER in vivo confocal microscopy Deep learning
DOI10.1186/s40662-023-00340-7
通讯作者Hong, Jing(hongjing196401@163.com)
英文摘要BackgroundThe goal of this study is to develop a fully automated segmentation and morphometric parameter estimation system for assessing abnormal corneal endothelial cells (CECs) from LASER in vivo confocal microscopy (IVCM) images.MethodsFirst, we developed a fully automated deep learning system for assessing abnormal CECs using a previous development set composed of normal images and a newly constructed development set composed of abnormal images. Second, two testing sets, one with 169 normal images and the other with 211 abnormal images, were used to evaluate the clinical validity and effectiveness of the proposed system on LASER IVCM images with different corneal endothelial conditions, particularly on abnormal images. Third, the automatically calculated endothelial cell density (ECD) and the manually calculated ECD were compared using both the previous and proposed systems.ResultsThe automated morphometric parameter estimations of the average number of cells, ECD, coefficient of variation in cell area and percentage of hexagonal cells were 257 cells, 2648 +/- 511 cells/mm(2), 32.18 +/- 6.70% and 56.23 +/- 8.69% for the normal CEC testing set and 83 cells, 1450 +/- 656 cells/mm(2), 34.87 +/- 10.53% and 42.55 +/- 20.64% for the abnormal CEC testing set. Furthermore, for the abnormal CEC testing set, Pearson's correlation coefficient between the automatically and manually calculated ECDs was 0.9447; the 95% limits of agreement between the manually and automatically calculated ECDs were between 329.0 and - 579.5 (concordance correlation coefficient = 0.93).ConclusionsThis is the first report to count and analyze the morphology of abnormal CECs in LASER IVCM images using deep learning. Deep learning produces highly objective evaluation indicators for LASER IVCM corneal endothelium images and greatly expands the range of applications for LASER IVCM.
WOS关键词DENSITY ; EYES
WOS研究方向Ophthalmology
语种英语
出版者BMC
WOS记录号WOS:001000258600001
源URL[http://ir.ia.ac.cn/handle/173211/53501]  
专题类脑芯片与系统研究
通讯作者Hong, Jing
作者单位1.Peking Univ Third Hosp, Dept Ophthalmol, 49 Garden North Rd, Beijing 100191, Peoples R China
2.Peking Univ Third Hosp, Beijing Key Lab Restorat Damaged Ocular Nerve, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Automat, Res Ctr Brain inspired Intelligence, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Qu, Jinghao,Qin, Xiaoran,Peng, Rongmei,et al. Assessing abnormal corneal endothelial cells from in vivo confocal microscopy images using a fully automated deep learning system[J]. EYE AND VISION,2023,10(1):10.
APA Qu, Jinghao.,Qin, Xiaoran.,Peng, Rongmei.,Xiao, Gege.,Gu, Shaofeng.,...&Hong, Jing.(2023).Assessing abnormal corneal endothelial cells from in vivo confocal microscopy images using a fully automated deep learning system.EYE AND VISION,10(1),10.
MLA Qu, Jinghao,et al."Assessing abnormal corneal endothelial cells from in vivo confocal microscopy images using a fully automated deep learning system".EYE AND VISION 10.1(2023):10.

入库方式： OAI收割

来源：自动化研究所

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