Using deep learning for robustness to parapapillary atrophy in optic disc segmentation

Ruchir Srivastava; Jun Cheng; Damon W.K. Wong; Jiang Liu

doi:10.1109/ISBI.2015.7163985

Using deep learning for robustness to parapapillary atrophy in optic disc segmentation

Ruchir Srivastava, Jun Cheng, Damon W.K. Wong, Jiang Liu

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

31 Citations (Scopus)

Abstract

Optic Disc (OD) segmentation from retinal fundus images is important for many applications such as detecting other optic structures and early detection of glaucoma. One of the major problems in segmenting OD is the presence of Para-papillary Atrophy (PPA) which in many cases looks similar to the OD. Researchers have used many different features to distinguish between PPA and OD, however each of these features has some limitation or the other. In this paper, we propose to use a deep neural network for OD segmentation which can learn features to distinguish PPA from OD. Using simple image intensity based features, the proposed method has the least mean overlapping error (9.7%) among the state-of-the-art works for OD segmentation in fundus images with PPA.

Original language	English
Title of host publication	2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
Publisher	IEEE Computer Society
Pages	768-771
Number of pages	4
ISBN (Electronic)	9781479923748
DOIs	https://doi.org/10.1109/ISBI.2015.7163985
Publication status	Published - 21 Jul 2015
Externally published	Yes
Event	12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States Duration: 16 Apr 2015 → 19 Apr 2015

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2015-July
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
Country/Territory	United States
City	Brooklyn
Period	16/04/15 → 19/04/15

Keywords

Optic Disc segmentation
deep learning
parapapillary atrophy
retinal fundus images

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/ISBI.2015.7163985

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Srivastava, R., Cheng, J., Wong, D. W. K., & Liu, J. (2015). Using deep learning for robustness to parapapillary atrophy in optic disc segmentation. In 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015 (pp. 768-771). Article 7163985 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2015-July). IEEE Computer Society. https://doi.org/10.1109/ISBI.2015.7163985

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title = "Using deep learning for robustness to parapapillary atrophy in optic disc segmentation",

abstract = "Optic Disc (OD) segmentation from retinal fundus images is important for many applications such as detecting other optic structures and early detection of glaucoma. One of the major problems in segmenting OD is the presence of Para-papillary Atrophy (PPA) which in many cases looks similar to the OD. Researchers have used many different features to distinguish between PPA and OD, however each of these features has some limitation or the other. In this paper, we propose to use a deep neural network for OD segmentation which can learn features to distinguish PPA from OD. Using simple image intensity based features, the proposed method has the least mean overlapping error (9.7%) among the state-of-the-art works for OD segmentation in fundus images with PPA.",

keywords = "Optic Disc segmentation, deep learning, parapapillary atrophy, retinal fundus images",

author = "Ruchir Srivastava and Jun Cheng and Wong, {Damon W.K.} and Jiang Liu",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 ; Conference date: 16-04-2015 Through 19-04-2015",

year = "2015",

month = jul,

day = "21",

doi = "10.1109/ISBI.2015.7163985",

language = "English",

series = "Proceedings - International Symposium on Biomedical Imaging",

publisher = "IEEE Computer Society",

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Srivastava, R, Cheng, J, Wong, DWK & Liu, J 2015, Using deep learning for robustness to parapapillary atrophy in optic disc segmentation. in 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015., 7163985, Proceedings - International Symposium on Biomedical Imaging, vol. 2015-July, IEEE Computer Society, pp. 768-771, 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, Brooklyn, United States, 16/04/15. https://doi.org/10.1109/ISBI.2015.7163985

Using deep learning for robustness to parapapillary atrophy in optic disc segmentation. / Srivastava, Ruchir; Cheng, Jun; Wong, Damon W.K. et al.
2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015. IEEE Computer Society, 2015. p. 768-771 7163985 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2015-July).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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T1 - Using deep learning for robustness to parapapillary atrophy in optic disc segmentation

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AB - Optic Disc (OD) segmentation from retinal fundus images is important for many applications such as detecting other optic structures and early detection of glaucoma. One of the major problems in segmenting OD is the presence of Para-papillary Atrophy (PPA) which in many cases looks similar to the OD. Researchers have used many different features to distinguish between PPA and OD, however each of these features has some limitation or the other. In this paper, we propose to use a deep neural network for OD segmentation which can learn features to distinguish PPA from OD. Using simple image intensity based features, the proposed method has the least mean overlapping error (9.7%) among the state-of-the-art works for OD segmentation in fundus images with PPA.

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Using deep learning for robustness to parapapillary atrophy in optic disc segmentation

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Keywords

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