Localization of Anatomical Features in Vascular-enhanced Enface OCT Images

Damon W.K. Wong; Ai Ping Yow; Bingyao Tan; Yao Xinwen; Jacqueline Chua; Leopold Schmetterer

doi:10.1109/EMBC44109.2020.9175868

Localization of Anatomical Features in Vascular-enhanced Enface OCT Images

Damon W.K. Wong, Ai Ping Yow, Bingyao Tan, Yao Xinwen, Jacqueline Chua, Leopold Schmetterer

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

2 Citations (Scopus)

Abstract

Optical coherence tomography (OCT) allows in vivo volumetric imaging of the eye. Identification and localization of anatomical features in enface OCT are important steps in OCT-based image analysis. However the visibility of anatomical features in both structural OCT or vascular OCT angiography is limited. In this paper, we propose to use vascular-enhanced enface OCT image for the concurrent detection of anatomical features, using a FasterRCNN object detection framework based on convolutional networks. Transfer learning was applied to adapt pre-trained models as the backbone networks. Models were evaluated on a dataset of 419 images. The results showed that VGG-FasterRCNN achieved a mean average precision 0.77, with localization errors of 0.18 ± 0.10 mm and 0.24 ± 0.13 mm for the macula and optic disc respectively. The results are promising and suggest that this network could potentially be used to automatically and concurrently detect anatomical features.Clinical Relevance - Localization of anatomical features in enface OCT is needed for the automation of OCT image analysis protocols. The use of fast detection networks could potentially suggest image-based real-time tracking during image acquisition.

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	1875-1878
Number of pages	4
ISBN (Electronic)	9781728119908
DOIs	https://doi.org/10.1109/EMBC44109.2020.9175868
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/EMBC44109.2020.9175868

Cite this

Wong, D. W. K., Yow, A. P., Tan, B., Xinwen, Y., Chua, J., & Schmetterer, L. (2020). Localization of Anatomical Features in Vascular-enhanced Enface OCT Images. In 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020 (pp. 1875-1878). Article 9175868 (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/EMBC44109.2020.9175868

Wong, Damon W.K. ; Yow, Ai Ping ; Tan, Bingyao et al. / Localization of Anatomical Features in Vascular-enhanced Enface OCT Images. 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. 1875-1878 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "Localization of Anatomical Features in Vascular-enhanced Enface OCT Images",

abstract = "Optical coherence tomography (OCT) allows in vivo volumetric imaging of the eye. Identification and localization of anatomical features in enface OCT are important steps in OCT-based image analysis. However the visibility of anatomical features in both structural OCT or vascular OCT angiography is limited. In this paper, we propose to use vascular-enhanced enface OCT image for the concurrent detection of anatomical features, using a FasterRCNN object detection framework based on convolutional networks. Transfer learning was applied to adapt pre-trained models as the backbone networks. Models were evaluated on a dataset of 419 images. The results showed that VGG-FasterRCNN achieved a mean average precision 0.77, with localization errors of 0.18 ± 0.10 mm and 0.24 ± 0.13 mm for the macula and optic disc respectively. The results are promising and suggest that this network could potentially be used to automatically and concurrently detect anatomical features.Clinical Relevance - Localization of anatomical features in enface OCT is needed for the automation of OCT image analysis protocols. The use of fast detection networks could potentially suggest image-based real-time tracking during image acquisition.",

author = "Wong, \{Damon W.K.\} and Yow, \{Ai Ping\} and Bingyao Tan and Yao Xinwen and Jacqueline Chua and Leopold Schmetterer",

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Wong, DWK, Yow, AP, Tan, B, Xinwen, Y, Chua, J & Schmetterer, L 2020, Localization of Anatomical Features in Vascular-enhanced Enface OCT Images. in 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020., 9175868, 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. 1875-1878, 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/EMBC44109.2020.9175868

Localization of Anatomical Features in Vascular-enhanced Enface OCT Images. / Wong, Damon W.K.; Yow, Ai Ping; Tan, Bingyao 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. 1875-1878 9175868 (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 - Wong, Damon W.K.

AU - Yow, Ai Ping

AU - Tan, Bingyao

AU - Xinwen, Yao

AU - Chua, Jacqueline

AU - Schmetterer, Leopold

PY - 2020/7

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N2 - Optical coherence tomography (OCT) allows in vivo volumetric imaging of the eye. Identification and localization of anatomical features in enface OCT are important steps in OCT-based image analysis. However the visibility of anatomical features in both structural OCT or vascular OCT angiography is limited. In this paper, we propose to use vascular-enhanced enface OCT image for the concurrent detection of anatomical features, using a FasterRCNN object detection framework based on convolutional networks. Transfer learning was applied to adapt pre-trained models as the backbone networks. Models were evaluated on a dataset of 419 images. The results showed that VGG-FasterRCNN achieved a mean average precision 0.77, with localization errors of 0.18 ± 0.10 mm and 0.24 ± 0.13 mm for the macula and optic disc respectively. The results are promising and suggest that this network could potentially be used to automatically and concurrently detect anatomical features.Clinical Relevance - Localization of anatomical features in enface OCT is needed for the automation of OCT image analysis protocols. The use of fast detection networks could potentially suggest image-based real-time tracking during image acquisition.

AB - Optical coherence tomography (OCT) allows in vivo volumetric imaging of the eye. Identification and localization of anatomical features in enface OCT are important steps in OCT-based image analysis. However the visibility of anatomical features in both structural OCT or vascular OCT angiography is limited. In this paper, we propose to use vascular-enhanced enface OCT image for the concurrent detection of anatomical features, using a FasterRCNN object detection framework based on convolutional networks. Transfer learning was applied to adapt pre-trained models as the backbone networks. Models were evaluated on a dataset of 419 images. The results showed that VGG-FasterRCNN achieved a mean average precision 0.77, with localization errors of 0.18 ± 0.10 mm and 0.24 ± 0.13 mm for the macula and optic disc respectively. The results are promising and suggest that this network could potentially be used to automatically and concurrently detect anatomical features.Clinical Relevance - Localization of anatomical features in enface OCT is needed for the automation of OCT image analysis protocols. The use of fast detection networks could potentially suggest image-based real-time tracking during image acquisition.

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Wong DWK, Yow AP, Tan B, Xinwen Y, Chua J, Schmetterer L. Localization of Anatomical Features in Vascular-enhanced Enface OCT Images. 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. 1875-1878. 9175868. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC44109.2020.9175868

Localization of Anatomical Features in Vascular-enhanced Enface OCT Images

Abstract

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