Automated Retinal Vascular Topological Information Extraction From OCTA

Ashe X. Lee; Ashish Saxena; Jacqueline Chua; Leopold Schmetterer; Bingyao Tan

doi:10.1109/EMBC48229.2022.9871160

Automated Retinal Vascular Topological Information Extraction From OCTA

Ashe X. Lee, Ashish Saxena, Jacqueline Chua, Leopold Schmetterer, Bingyao Tan

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

1 Citation (Scopus)

Abstract

The retinal vascular system adapts and reacts rapidly to ocular diseases such as glaucoma, diabetic retinopathy and age-related macular degeneration. Here we present a combination of methods to further extract vascular information from 12× 12mm wide-field optical coherence tomography angiography (OCTA). An integrated U-Net for the segmentation and classification of arteries and veins reached a segmentation IoU of 0.7095-0.0224, and classification IoU of 0.8793-0.1049 and 0.8928-0.0929 respectively. A correcting algorithm which uses topological information was created to correct the misclassification and connectivity of the vessels, which showed an average increase of 8.29% in IoU. Finally, the vessel morphometry of branch orders was extracted, where this allows the direct comparison of artery/vein, arterioles/venules and capillaries.

Original language	English
Title of host publication	44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1839-1842
Number of pages	4
ISBN (Electronic)	9781728127828
DOIs	https://doi.org/10.1109/EMBC48229.2022.9871160
Publication status	Published - 2022
Externally published	Yes
Event	44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022 - Glasgow, United Kingdom Duration: Jul 11 2022 → Jul 15 2022

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	2022-July
ISSN (Print)	1557-170X

Conference

Conference	44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
Country/Territory	United Kingdom
City	Glasgow
Period	7/11/22 → 7/15/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

ASJC Scopus Subject Areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/EMBC48229.2022.9871160

Cite this

Lee, A. X., Saxena, A., Chua, J., Schmetterer, L., & Tan, B. (2022). Automated Retinal Vascular Topological Information Extraction From OCTA. In 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022 (pp. 1839-1842). (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2022-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC48229.2022.9871160

Lee, Ashe X. ; Saxena, Ashish ; Chua, Jacqueline et al. / Automated Retinal Vascular Topological Information Extraction From OCTA. 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 1839-1842 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "Automated Retinal Vascular Topological Information Extraction From OCTA",

abstract = "The retinal vascular system adapts and reacts rapidly to ocular diseases such as glaucoma, diabetic retinopathy and age-related macular degeneration. Here we present a combination of methods to further extract vascular information from 12× 12mm wide-field optical coherence tomography angiography (OCTA). An integrated U-Net for the segmentation and classification of arteries and veins reached a segmentation IoU of 0.7095-0.0224, and classification IoU of 0.8793-0.1049 and 0.8928-0.0929 respectively. A correcting algorithm which uses topological information was created to correct the misclassification and connectivity of the vessels, which showed an average increase of 8.29\% in IoU. Finally, the vessel morphometry of branch orders was extracted, where this allows the direct comparison of artery/vein, arterioles/venules and capillaries.",

author = "Lee, \{Ashe X.\} and Ashish Saxena and Jacqueline Chua and Leopold Schmetterer and Bingyao Tan",

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year = "2022",

doi = "10.1109/EMBC48229.2022.9871160",

language = "English",

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

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Lee, AX, Saxena, A, Chua, J, Schmetterer, L & Tan, B 2022, Automated Retinal Vascular Topological Information Extraction From OCTA. in 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2022-July, Institute of Electrical and Electronics Engineers Inc., pp. 1839-1842, 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022, Glasgow, United Kingdom, 7/11/22. https://doi.org/10.1109/EMBC48229.2022.9871160

Automated Retinal Vascular Topological Information Extraction From OCTA. / Lee, Ashe X.; Saxena, Ashish; Chua, Jacqueline et al.
44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 1839-1842 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2022-July).

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

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AU - Schmetterer, Leopold

AU - Tan, Bingyao

PY - 2022

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N2 - The retinal vascular system adapts and reacts rapidly to ocular diseases such as glaucoma, diabetic retinopathy and age-related macular degeneration. Here we present a combination of methods to further extract vascular information from 12× 12mm wide-field optical coherence tomography angiography (OCTA). An integrated U-Net for the segmentation and classification of arteries and veins reached a segmentation IoU of 0.7095-0.0224, and classification IoU of 0.8793-0.1049 and 0.8928-0.0929 respectively. A correcting algorithm which uses topological information was created to correct the misclassification and connectivity of the vessels, which showed an average increase of 8.29% in IoU. Finally, the vessel morphometry of branch orders was extracted, where this allows the direct comparison of artery/vein, arterioles/venules and capillaries.

AB - The retinal vascular system adapts and reacts rapidly to ocular diseases such as glaucoma, diabetic retinopathy and age-related macular degeneration. Here we present a combination of methods to further extract vascular information from 12× 12mm wide-field optical coherence tomography angiography (OCTA). An integrated U-Net for the segmentation and classification of arteries and veins reached a segmentation IoU of 0.7095-0.0224, and classification IoU of 0.8793-0.1049 and 0.8928-0.0929 respectively. A correcting algorithm which uses topological information was created to correct the misclassification and connectivity of the vessels, which showed an average increase of 8.29% in IoU. Finally, the vessel morphometry of branch orders was extracted, where this allows the direct comparison of artery/vein, arterioles/venules and capillaries.

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Lee AX, Saxena A, Chua J, Schmetterer L, Tan B. Automated Retinal Vascular Topological Information Extraction From OCTA. In 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 1839-1842. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC48229.2022.9871160

Automated Retinal Vascular Topological Information Extraction From OCTA

Abstract

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Bibliographical note

ASJC Scopus Subject Areas

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