TY - GEN
T1 - Anterior Chamber Angles Classification in Anterior Segment OCT Images via Multi-Scale Regions Convolutional Neural Networks
AU - Hao, Huaying
AU - Zhao, Yitian
AU - Fu, Huazhu
AU - Shang, Qiaoling
AU - Li, Fei
AU - Zhang, Xiulan
AU - Liu, Jiang
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85077862529&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2019.8857615
DO - 10.1109/EMBC.2019.8857615
M3 - Conference contribution
C2 - 31946028
AN - SCOPUS:85077862529
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 849
EP - 852
BT - 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Y2 - 23 July 2019 through 27 July 2019
ER -