A Framework for the Discovery of Retinal Biomarkers in Optical Coherence Tomography Angiography (OCTA)

Ylenia Giarratano; Alisa Pavel; Jie Lian; Rayna Andreeva; Alessandro Fontanella; Rik Sarkar; Laura J. Reid; Shareen Forbes; Dan Pugh; Tariq E. Farrah; Neeraj Dhaun; Baljean Dhillon; Tom MacGillivray; Miguel O. Bernabeu

doi:10.1007/978-3-030-63419-3_16

A Framework for the Discovery of Retinal Biomarkers in Optical Coherence Tomography Angiography (OCTA)

Ylenia Giarratano, Alisa Pavel, Jie Lian, Rayna Andreeva, Alessandro Fontanella, Rik Sarkar, Laura J. Reid, Shareen Forbes, Dan Pugh, Tariq E. Farrah, Neeraj Dhaun, Baljean Dhillon, Tom MacGillivray, Miguel O. Bernabeu

BioMediTech

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

Abstract

Recent studies have demonstrated the potential of OCTA retinal imaging for the discovery of biomarkers of vascular disease of the eye and other organs. Furthermore, advances in deep learning have made it possible to train algorithms for the automated detection of such biomarkers. However, two key limitations of this approach are the need for large numbers of labeled images to train the algorithms, which are often not met by the typical single-centre prospective studies in the literature, and the lack of interpretability of the features learned during training. In the current study, we developed a network analysis framework to characterise retinal vasculature where geometric and topological information are exploited to increase the performance of classifiers trained on tens of OCTA images. We demonstrate our approach in two different diseases with a retinal vascular footprint: diabetic retinopathy (DR) and chronic kidney disease (CKD). Our approach enables the discovery of previously unreported retinal vascular morphological differences in DR and CKD, and demonstrate the potential of OCTA for automated disease assessment.

Original language	English
Title of host publication	Ophthalmic Medical Image Analysis
Subtitle of host publication	7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings
Editors	Huazhu Fu, Mona K. Garvin, Tom MacGillivray, Yanwu Xu, Yalin Zheng
Place of Publication	Cham
Publisher	Springer
Pages	155-164
Number of pages	10
ISBN (Electronic)	978-3-030-63419-3
ISBN (Print)	978-3-030-63418-6
DOIs	https://doi.org/10.1007/978-3-030-63419-3_16
Publication status	Published - 2020
Publication type	A4 Article in conference proceedings
Event	International Workshop on Ophthalmic Medical Image Analysis - Lima, Peru Duration: 8 Oct 2020 → 8 Oct 2020 Conference number: 7th

Publication series

Name	Lecture Notes in Computer Science
Volume	12069
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Workshop on Ophthalmic Medical Image Analysis
Abbreviated title	OMIA
Country/Territory	Peru
City	Lima
Period	8/10/20 → 8/10/20

Keywords

Graph analysis
Optical coherence tomography angiography
Retinal biomarkers
Vascular network

Publication forum classification

Publication forum level 1

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-63419-3_16

Cite this

Giarratano, Y., Pavel, A., Lian, J., Andreeva, R., Fontanella, A., Sarkar, R., Reid, L. J., Forbes, S., Pugh, D., Farrah, T. E., Dhaun, N., Dhillon, B., MacGillivray, T., & Bernabeu, M. O. (2020). A Framework for the Discovery of Retinal Biomarkers in Optical Coherence Tomography Angiography (OCTA). In H. Fu, M. K. Garvin, T. MacGillivray, Y. Xu, & Y. Zheng (Eds.), Ophthalmic Medical Image Analysis: 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings (pp. 155-164). (Lecture Notes in Computer Science; Vol. 12069). Springer. https://doi.org/10.1007/978-3-030-63419-3_16

Giarratano, Ylenia ; Pavel, Alisa ; Lian, Jie et al. / A Framework for the Discovery of Retinal Biomarkers in Optical Coherence Tomography Angiography (OCTA). Ophthalmic Medical Image Analysis: 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings. editor / Huazhu Fu ; Mona K. Garvin ; Tom MacGillivray ; Yanwu Xu ; Yalin Zheng. Cham : Springer, 2020. pp. 155-164 (Lecture Notes in Computer Science).

@inproceedings{74ec0d824ac646fcbec305eabbd981e6,

title = "A Framework for the Discovery of Retinal Biomarkers in Optical Coherence Tomography Angiography (OCTA)",

abstract = "Recent studies have demonstrated the potential of OCTA retinal imaging for the discovery of biomarkers of vascular disease of the eye and other organs. Furthermore, advances in deep learning have made it possible to train algorithms for the automated detection of such biomarkers. However, two key limitations of this approach are the need for large numbers of labeled images to train the algorithms, which are often not met by the typical single-centre prospective studies in the literature, and the lack of interpretability of the features learned during training. In the current study, we developed a network analysis framework to characterise retinal vasculature where geometric and topological information are exploited to increase the performance of classifiers trained on tens of OCTA images. We demonstrate our approach in two different diseases with a retinal vascular footprint: diabetic retinopathy (DR) and chronic kidney disease (CKD). Our approach enables the discovery of previously unreported retinal vascular morphological differences in DR and CKD, and demonstrate the potential of OCTA for automated disease assessment.",

keywords = "Graph analysis, Optical coherence tomography angiography, Retinal biomarkers, Vascular network",

author = "Ylenia Giarratano and Alisa Pavel and Jie Lian and Rayna Andreeva and Alessandro Fontanella and Rik Sarkar and Reid, {Laura J.} and Shareen Forbes and Dan Pugh and Farrah, {Tariq E.} and Neeraj Dhaun and Baljean Dhillon and Tom MacGillivray and Bernabeu, {Miguel O.}",

note = "JUFOID=62555 Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; International Workshop on Ophthalmic Medical Image Analysis, OMIA ; Conference date: 08-10-2020 Through 08-10-2020",

year = "2020",

doi = "10.1007/978-3-030-63419-3_16",

language = "English",

isbn = "978-3-030-63418-6",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "155--164",

editor = "Huazhu Fu and Garvin, {Mona K.} and Tom MacGillivray and Yanwu Xu and Yalin Zheng",

booktitle = "Ophthalmic Medical Image Analysis",

}

Giarratano, Y, Pavel, A, Lian, J, Andreeva, R, Fontanella, A, Sarkar, R, Reid, LJ, Forbes, S, Pugh, D, Farrah, TE, Dhaun, N, Dhillon, B, MacGillivray, T & Bernabeu, MO 2020, A Framework for the Discovery of Retinal Biomarkers in Optical Coherence Tomography Angiography (OCTA). in H Fu, MK Garvin, T MacGillivray, Y Xu & Y Zheng (eds), Ophthalmic Medical Image Analysis: 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings. Lecture Notes in Computer Science, vol. 12069, Springer, Cham, pp. 155-164, International Workshop on Ophthalmic Medical Image Analysis, Lima, Peru, 8/10/20. https://doi.org/10.1007/978-3-030-63419-3_16

A Framework for the Discovery of Retinal Biomarkers in Optical Coherence Tomography Angiography (OCTA). / Giarratano, Ylenia; Pavel, Alisa; Lian, Jie et al.
Ophthalmic Medical Image Analysis: 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings. ed. / Huazhu Fu; Mona K. Garvin; Tom MacGillivray; Yanwu Xu; Yalin Zheng. Cham: Springer, 2020. p. 155-164 (Lecture Notes in Computer Science; Vol. 12069).

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

TY - GEN

T1 - A Framework for the Discovery of Retinal Biomarkers in Optical Coherence Tomography Angiography (OCTA)

AU - Giarratano, Ylenia

AU - Pavel, Alisa

AU - Lian, Jie

AU - Andreeva, Rayna

AU - Fontanella, Alessandro

AU - Sarkar, Rik

AU - Reid, Laura J.

AU - Forbes, Shareen

AU - Pugh, Dan

AU - Farrah, Tariq E.

AU - Dhaun, Neeraj

AU - Dhillon, Baljean

AU - MacGillivray, Tom

AU - Bernabeu, Miguel O.

N1 - Conference code: 7th

PY - 2020

Y1 - 2020

N2 - Recent studies have demonstrated the potential of OCTA retinal imaging for the discovery of biomarkers of vascular disease of the eye and other organs. Furthermore, advances in deep learning have made it possible to train algorithms for the automated detection of such biomarkers. However, two key limitations of this approach are the need for large numbers of labeled images to train the algorithms, which are often not met by the typical single-centre prospective studies in the literature, and the lack of interpretability of the features learned during training. In the current study, we developed a network analysis framework to characterise retinal vasculature where geometric and topological information are exploited to increase the performance of classifiers trained on tens of OCTA images. We demonstrate our approach in two different diseases with a retinal vascular footprint: diabetic retinopathy (DR) and chronic kidney disease (CKD). Our approach enables the discovery of previously unreported retinal vascular morphological differences in DR and CKD, and demonstrate the potential of OCTA for automated disease assessment.

AB - Recent studies have demonstrated the potential of OCTA retinal imaging for the discovery of biomarkers of vascular disease of the eye and other organs. Furthermore, advances in deep learning have made it possible to train algorithms for the automated detection of such biomarkers. However, two key limitations of this approach are the need for large numbers of labeled images to train the algorithms, which are often not met by the typical single-centre prospective studies in the literature, and the lack of interpretability of the features learned during training. In the current study, we developed a network analysis framework to characterise retinal vasculature where geometric and topological information are exploited to increase the performance of classifiers trained on tens of OCTA images. We demonstrate our approach in two different diseases with a retinal vascular footprint: diabetic retinopathy (DR) and chronic kidney disease (CKD). Our approach enables the discovery of previously unreported retinal vascular morphological differences in DR and CKD, and demonstrate the potential of OCTA for automated disease assessment.

KW - Graph analysis

KW - Optical coherence tomography angiography

KW - Retinal biomarkers

KW - Vascular network

U2 - 10.1007/978-3-030-63419-3_16

DO - 10.1007/978-3-030-63419-3_16

M3 - Conference contribution

AN - SCOPUS:85097421338

SN - 978-3-030-63418-6

T3 - Lecture Notes in Computer Science

SP - 155

EP - 164

BT - Ophthalmic Medical Image Analysis

A2 - Fu, Huazhu

A2 - Garvin, Mona K.

A2 - MacGillivray, Tom

A2 - Xu, Yanwu

A2 - Zheng, Yalin

PB - Springer

CY - Cham

T2 - International Workshop on Ophthalmic Medical Image Analysis

Y2 - 8 October 2020 through 8 October 2020

ER -

Giarratano Y, Pavel A, Lian J, Andreeva R, Fontanella A, Sarkar R et al. A Framework for the Discovery of Retinal Biomarkers in Optical Coherence Tomography Angiography (OCTA). In Fu H, Garvin MK, MacGillivray T, Xu Y, Zheng Y, editors, Ophthalmic Medical Image Analysis: 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings. Cham: Springer. 2020. p. 155-164. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-63419-3_16

A Framework for the Discovery of Retinal Biomarkers in Optical Coherence Tomography Angiography (OCTA)

Abstract

Publication series

Conference

Keywords

Publication forum classification

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this