TY - GEN
T1 - Localizing Optic Disc and Cup for Glaucoma Screening via Deep Object Detection Networks
AU - Sun, Xu
AU - Xu, Yanwu
AU - Tan, Mingkui
AU - Fu, Huazhu
AU - Zhao, Wei
AU - You, Tianyuan
AU - Liu, Jiang
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - Segmentation of the optic disc (OD) and optic cup (OC) from a retinal fundus image plays an important role for glaucoma screening and diagnosis. However, most existing methods only focus on pixel-level representations, and ignore the high level representations. In this work, we consider the high level concept, i.e., objectness constraint, for fundus structure analysis. Specifically, we introduce a deep object detection network to localize OD and OC simultaneously. The end-to-end architecture guarantees to learn more discriminative representations. Moreover, data from a similar domain can further contributes to our algorithm through transfer learning techniques. Experimental results show that our method achieves state-of-the-art OD and OC segmentation/localization results on ORIGA dataset. Moreover, the proposed method also obtains satisfactory glaucoma screening performance with the calculated vertical cup-to-disc ratio (CDR).
AB - Segmentation of the optic disc (OD) and optic cup (OC) from a retinal fundus image plays an important role for glaucoma screening and diagnosis. However, most existing methods only focus on pixel-level representations, and ignore the high level representations. In this work, we consider the high level concept, i.e., objectness constraint, for fundus structure analysis. Specifically, we introduce a deep object detection network to localize OD and OC simultaneously. The end-to-end architecture guarantees to learn more discriminative representations. Moreover, data from a similar domain can further contributes to our algorithm through transfer learning techniques. Experimental results show that our method achieves state-of-the-art OD and OC segmentation/localization results on ORIGA dataset. Moreover, the proposed method also obtains satisfactory glaucoma screening performance with the calculated vertical cup-to-disc ratio (CDR).
UR - http://www.scopus.com/inward/record.url?scp=85053924967&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00949-6_28
DO - 10.1007/978-3-030-00949-6_28
M3 - Conference contribution
AN - SCOPUS:85053924967
SN - 9783030009489
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 236
EP - 244
BT - Computational Pathology and Ophthalmic Medical Image Analysis - First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Proceedings
A2 - Taylor, Zeike
A2 - Bogunovic, Hrvoje
A2 - Snead, David
A2 - Garvin, Mona K.
A2 - Chen, Xin Jan
A2 - Ciompi, Francesco
A2 - Xu, Yanwu
A2 - Maier-Hein, Lena
A2 - Veta, Mitko
A2 - Trucco, Emanuele
A2 - Stoyanov, Danail
A2 - Rajpoot, Nasir
A2 - van der Laak, Jeroen
A2 - Martel, Anne
A2 - McKenna, Stephen
PB - Springer Verlag
T2 - 1st International Workshop on Computational Pathology, COMPAY 2018 and 5th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2018 Held in Conjunction with MICCAI 2018
Y2 - 16 September 2018 through 20 September 2018
ER -