Anterior Chamber Angles Classification in Anterior Segment OCT Images via Multi-Scale Regions Convolutional Neural Networks

Hao, Huaying1,2,3; Zhao, Yitian1,2; Fu, Huazhu4; Shang, Qiaoling1,2; Li, Fei2,5; Zhang, Xiulan2,5; Liu, Jiang1,2,6

10.1109/EMBC.2019.8857615

2019

1557-170X

1558-4615

978-1-5386-1312-2

41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019

849-852

23-27 July 2019

Berlin, Germany

345 E 47TH ST, NEW YORK, NY 10017 USA

Institute of Electrical and Electronics Engineers Inc.

Angle-closure glaucoma is one of the major causes of blindness in Asia. In this paper, we present a new approach for the classification of the anterior chamber angles into open, narrowed, and closure, in anterior segment optical coherence tomography (AS-OCT), by learning the manual annotations from gonioscopy, so as to further assist the assessment of angle-closure glaucoma. The proposed framework firstly localizes the anterior chamber angle region automatically, which is the primary structural image cue for clinically identifying glaucoma. Then three scales of cropped chamber angle images are fed into our Multi-Scale Regions Convolutional Neural Networks (MSRCNN) architecture, in which three parallel convolutional neural networks are applied to extract feature representations. Finally, the representations are stacked to fully-connected layer for glaucoma type classification. The proposed method is evaluated across a dataset of 9728 anterior chamber angle images, and the experimental results show that the proposed method outperforms existing state-of-the-art methods in applicability, effectiveness, and accuracy.
© 2019 IEEE.

Picture archiving and communication systems Image segmentation Streaming media Convolutional neural networks Deep learning Predictive models Blindness

其他

英语

EI ; CPCI-S

Natural Science Foundation of Ningbo[2018A610055] ; Guangzhou Science and Technology Program key projects[201803010066] ; National Natural Science Foundation of China[61601029]

Engineering

Engineering, Biomedical ; Engineering, Electrical & Electronic

WOS:000557295301065

20200308034499

Ophthalmology ; Optical tomography ; Image classification ; Image segmentation ; Convolutional neural networks

Medicine and Pharmacology:461.6 ; Information Theory and Signal Processing:716.1 ; Data Processing and Image Processing:723.2 ; Optical Devices and Systems:741.3

EV Compendex

1.Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Ningbo, China
2.Glaucoma Artificial Intelligence Diagnosis and Imaging Analysis Joint Research Lab, Guangzhou& Ningbo, China
3.Faculty of Mechanical Engineering and Mechanics, Ningbo University, Ningbo, China
4.Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates
5.Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat-sen University, Guangzhou, China
6.Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

Hao, Huaying,Zhao, Yitian,Fu, Huazhu,et al. Anterior Chamber Angles Classification in Anterior Segment OCT Images via Multi-Scale Regions Convolutional Neural Networks[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:Institute of Electrical and Electronics Engineers Inc.,2019:849-852.