Automated circumpapillary retinal nerve fiber layer segmentation in high-resolution swept-source OCT

Ai Ping Yow; Bingyao Tan; Jacqueline Chua; Tin Aung; Rahat Husain; Leopold Schmetterer; Damon Wong

doi:10.1109/EMBC44109.2020.9175828

Automated circumpapillary retinal nerve fiber layer segmentation in high-resolution swept-source OCT

Ai Ping Yow, Bingyao Tan, Jacqueline Chua, Tin Aung, Rahat Husain, Leopold Schmetterer, Damon Wong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

Glaucoma is a progressive optic neuropathy that leads to loss of retinal ganglion cells and thinning of retinal nerve fiber layer (RNFL). Circumpapillary RNFL thickness measurements have been used for glaucoma diagnostic and monitoring purposes. However, manual measurement of the RNFL thickness is tedious and subjective. We proposed and evaluated the performance of automated RNFL segmentation from OCT images using a state-of-the-art deep learning-based model. Circumpapillary OCT scans were extracted from volumetric OCT scans using a high-resolution swept-source OCT device. Manual annotation was performed on the extracted scans and used for training and evaluation. The results show that the accuracy and diagnostic performance is comparable to manual assessment, and the potential application of deep learning-based approach in such segmentation.

Original language	English
Title of host publication	42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publication	Enabling Innovative Technologies for Global Healthcare, EMBC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1832-1835
Number of pages	4
ISBN (Electronic)	9781728119908
DOIs	https://doi.org/10.1109/EMBC44109.2020.9175828
Publication status	Published - Jul 2020
Externally published	Yes
Event	42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, Canada Duration: Jul 20 2020 → Jul 24 2020

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	2020-July
ISSN (Print)	1557-170X

Conference

Conference	42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
Country/Territory	Canada
City	Montreal
Period	7/20/20 → 7/24/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

ASJC Scopus Subject Areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/EMBC44109.2020.9175828

Cite this

Yow, A. P., Tan, B., Chua, J., Aung, T., Husain, R., Schmetterer, L., & Wong, D. (2020). Automated circumpapillary retinal nerve fiber layer segmentation in high-resolution swept-source OCT. In 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020 (pp. 1832-1835). Article 9175828 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2020-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC44109.2020.9175828

Yow, Ai Ping ; Tan, Bingyao ; Chua, Jacqueline et al. / Automated circumpapillary retinal nerve fiber layer segmentation in high-resolution swept-source OCT. 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1832-1835 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "Automated circumpapillary retinal nerve fiber layer segmentation in high-resolution swept-source OCT",

abstract = "Glaucoma is a progressive optic neuropathy that leads to loss of retinal ganglion cells and thinning of retinal nerve fiber layer (RNFL). Circumpapillary RNFL thickness measurements have been used for glaucoma diagnostic and monitoring purposes. However, manual measurement of the RNFL thickness is tedious and subjective. We proposed and evaluated the performance of automated RNFL segmentation from OCT images using a state-of-the-art deep learning-based model. Circumpapillary OCT scans were extracted from volumetric OCT scans using a high-resolution swept-source OCT device. Manual annotation was performed on the extracted scans and used for training and evaluation. The results show that the accuracy and diagnostic performance is comparable to manual assessment, and the potential application of deep learning-based approach in such segmentation.",

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language = "English",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

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Yow, AP, Tan, B, Chua, J, Aung, T, Husain, R, Schmetterer, L & Wong, D 2020, Automated circumpapillary retinal nerve fiber layer segmentation in high-resolution swept-source OCT. in 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020., 9175828, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2020-July, Institute of Electrical and Electronics Engineers Inc., pp. 1832-1835, 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020, Montreal, Canada, 7/20/20. https://doi.org/10.1109/EMBC44109.2020.9175828

Automated circumpapillary retinal nerve fiber layer segmentation in high-resolution swept-source OCT. / Yow, Ai Ping; Tan, Bingyao; Chua, Jacqueline et al.
42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1832-1835 9175828 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2020-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Yow, Ai Ping

AU - Tan, Bingyao

AU - Chua, Jacqueline

AU - Aung, Tin

AU - Husain, Rahat

AU - Schmetterer, Leopold

AU - Wong, Damon

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N2 - Glaucoma is a progressive optic neuropathy that leads to loss of retinal ganglion cells and thinning of retinal nerve fiber layer (RNFL). Circumpapillary RNFL thickness measurements have been used for glaucoma diagnostic and monitoring purposes. However, manual measurement of the RNFL thickness is tedious and subjective. We proposed and evaluated the performance of automated RNFL segmentation from OCT images using a state-of-the-art deep learning-based model. Circumpapillary OCT scans were extracted from volumetric OCT scans using a high-resolution swept-source OCT device. Manual annotation was performed on the extracted scans and used for training and evaluation. The results show that the accuracy and diagnostic performance is comparable to manual assessment, and the potential application of deep learning-based approach in such segmentation.

AB - Glaucoma is a progressive optic neuropathy that leads to loss of retinal ganglion cells and thinning of retinal nerve fiber layer (RNFL). Circumpapillary RNFL thickness measurements have been used for glaucoma diagnostic and monitoring purposes. However, manual measurement of the RNFL thickness is tedious and subjective. We proposed and evaluated the performance of automated RNFL segmentation from OCT images using a state-of-the-art deep learning-based model. Circumpapillary OCT scans were extracted from volumetric OCT scans using a high-resolution swept-source OCT device. Manual annotation was performed on the extracted scans and used for training and evaluation. The results show that the accuracy and diagnostic performance is comparable to manual assessment, and the potential application of deep learning-based approach in such segmentation.

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Yow AP, Tan B, Chua J, Aung T, Husain R, Schmetterer L et al. Automated circumpapillary retinal nerve fiber layer segmentation in high-resolution swept-source OCT. In 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1832-1835. 9175828. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC44109.2020.9175828

Automated circumpapillary retinal nerve fiber layer segmentation in high-resolution swept-source OCT

Abstract

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Bibliographical note

ASJC Scopus Subject Areas

Access to Document

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