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Optical coherence tomography-based deep learning algorithm for quantification of the location of the intraocular lens

文献类型：期刊论文

作者Xin, Chen4; Bian, Gui-Bin3; Zhang, Haojie1; Liu, Weipeng1; Dong, Zhe2
刊名ANNALS OF TRANSLATIONAL MEDICINE
出版日期2020-07-01
卷号8期号:14页码:11
关键词Intraocular lens optical coherence tomography deep learning position
ISSN号2305-5839
DOI10.21037/atm-20-4706
通讯作者Dong, Zhe(dongzhe0@126.com)
英文摘要Background: Cataract surgery has been recently developed from sight rehabilitating surgery to accurate refractive surgery. The precise concentration of intraocular lens (IOL) is crucial for postoperative high visual quanlity. The three-dimentional (3D) images of ocular anterior segment captured by optial coherence tomography (OCT) make it possible to evaluate the IOL position in 3D space, which provide insights into factors relavant to the visual quanlity and better design of new functional IOL. The deep learning algorithm potentially quantify the IOL position in an objective and efficient way. Methods: The region-based fully convolutional network (R-FCN) was used to recogonize and delineate the IOL configuration in 3D OCT images. Scleral spur was identified automatically. Then the tilt angle of the IOL relative to the scleral spur plane along with its decentration with respect to the pupil were calculated. Repeatability and reliability of the method was evaluated by the intraclass correlation coefficient. Results: After improvement, the R-FCN network recognition efficiency of IOL configuration reached 0.910. The ICC of reliability and repeatability of the method is 0.867 and 0.901. The average tilt angle of the IOL relative to scleral spur is located in 1.65 +/- 1.00 degrees. The offsets dx and dy occurring in the early X and Y directions of the IOL are 0.29 +/- 0.22 and 0.33 +/- 0.24 mm, respectively. The IOL offset distance is 0.44 +/- 0.33 mm. Conclusions: We proposed a practical method to quantify the IOL postion in 3D space based on OCT images and assisted by an algorithm.
WOS关键词DECENTRATION ; TILT
资助项目Beijing Municipal Administration of Hospitals' Youth Programme[QML20180202] ; National Natural Science Foundation of China[U1713220]
WOS研究方向Oncology ; Research & Experimental Medicine
语种英语
WOS记录号WOS:000554494600002
出版者AME PUBL CO
资助机构Beijing Municipal Administration of Hospitals' Youth Programme ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/40372]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
通讯作者Dong, Zhe
作者单位1.Hebei Univ Technol, Coll Artificial Intelligence & Data Sci, Tianjin, Peoples R China
2.Capital Med Univ, Beijing Tongren Hosp, Beijing Key Lab Ophthalmol & Visual Sci, Beijing Tongren Eye Ctr, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
4.Capital Med Univ, Beijing Tongren Hosp, Beijing Inst Ophthalmol, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Xin, Chen,Bian, Gui-Bin,Zhang, Haojie,et al. Optical coherence tomography-based deep learning algorithm for quantification of the location of the intraocular lens[J]. ANNALS OF TRANSLATIONAL MEDICINE,2020,8(14):11.
APA Xin, Chen,Bian, Gui-Bin,Zhang, Haojie,Liu, Weipeng,&Dong, Zhe.(2020).Optical coherence tomography-based deep learning algorithm for quantification of the location of the intraocular lens.ANNALS OF TRANSLATIONAL MEDICINE,8(14),11.
MLA Xin, Chen,et al."Optical coherence tomography-based deep learning algorithm for quantification of the location of the intraocular lens".ANNALS OF TRANSLATIONAL MEDICINE 8.14(2020):11.

入库方式： OAI收割

来源：自动化研究所

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