Conditional GAN for Prediction of Glaucoma Progression with Macular Optical Coherence Tomography

Osama N. Hassan; Serhat Sahin; Vahid Mohammadzadeh; Xiaohe Yang; Navid Amini; Apoorva Mylavarapu; Jack Martinyan; Tae Hong; Golnoush Mahmoudinezhad; Daniel Rueckert; Kouros Nouri-Mahdavi; Fabien Scalzo

doi:10.1007/978-3-030-64559-5_61

Conditional GAN for Prediction of Glaucoma Progression with Macular Optical Coherence Tomography

Osama N. Hassan, Serhat Sahin, Vahid Mohammadzadeh, Xiaohe Yang, Navid Amini, Apoorva Mylavarapu, Jack Martinyan, Tae Hong, Golnoush Mahmoudinezhad, Daniel Rueckert, Kouros Nouri-Mahdavi, Fabien Scalzo

Informatics 31 - Chair of Artificial Intelligence in Healthcare and Medicine

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

9 Scopus citations

Abstract

The estimation of glaucoma progression is a challenging task as the rate of disease progression varies among individuals in addition to other factors such as measurement variability and the lack of standardization in defining progression. Structural tests, such as thickness measurements of the retinal nerve fiber layer or the macula with optical coherence tomography (OCT), are able to detect anatomical changes in glaucomatous eyes. Such changes may be observed before any functional damage. In this work, we built a generative deep learning model using the conditional GAN architecture to predict glaucoma progression over time. The patient’s OCT scan is predicted from three or two prior measurements. The predicted images demonstrate high similarity with the ground truth images. In addition, our results suggest that OCT scans obtained from only two prior visits may actually be sufficient to predict the next OCT scan of the patient after six months.

Original language	English
Title of host publication	Advances in Visual Computing - 15th International Symposium, ISVC 2020, Proceedings
Editors	George Bebis, Zhaozheng Yin, Edward Kim, Jan Bender, Kartic Subr, Bum Chul Kwon, Jian Zhao, Denis Kalkofen, George Baciu
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	761-772
Number of pages	12
ISBN (Print)	9783030645588
DOIs	https://doi.org/10.1007/978-3-030-64559-5_61
State	Published - 2020
Event	15th International Symposium on Visual Computing, ISVC 2020 - San Diego, United States Duration: 5 Oct 2020 → 7 Oct 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12510 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Symposium on Visual Computing, ISVC 2020
Country/Territory	United States
City	San Diego
Period	5/10/20 → 7/10/20

Keywords

CGAN
Generative models
Glaucoma progression
OCT

Access to Document

10.1007/978-3-030-64559-5_61

Cite this

Hassan, O. N., Sahin, S., Mohammadzadeh, V., Yang, X., Amini, N., Mylavarapu, A., Martinyan, J., Hong, T., Mahmoudinezhad, G., Rueckert, D., Nouri-Mahdavi, K., & Scalzo, F. (2020). Conditional GAN for Prediction of Glaucoma Progression with Macular Optical Coherence Tomography. In G. Bebis, Z. Yin, E. Kim, J. Bender, K. Subr, B. C. Kwon, J. Zhao, D. Kalkofen, & G. Baciu (Eds.), Advances in Visual Computing - 15th International Symposium, ISVC 2020, Proceedings (pp. 761-772). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12510 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-64559-5_61

Hassan, Osama N. ; Sahin, Serhat ; Mohammadzadeh, Vahid et al. / Conditional GAN for Prediction of Glaucoma Progression with Macular Optical Coherence Tomography. Advances in Visual Computing - 15th International Symposium, ISVC 2020, Proceedings. editor / George Bebis ; Zhaozheng Yin ; Edward Kim ; Jan Bender ; Kartic Subr ; Bum Chul Kwon ; Jian Zhao ; Denis Kalkofen ; George Baciu. Springer Science and Business Media Deutschland GmbH, 2020. pp. 761-772 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{856dcff894c54f9b8b5f8197a5b09c28,

title = "Conditional GAN for Prediction of Glaucoma Progression with Macular Optical Coherence Tomography",

abstract = "The estimation of glaucoma progression is a challenging task as the rate of disease progression varies among individuals in addition to other factors such as measurement variability and the lack of standardization in defining progression. Structural tests, such as thickness measurements of the retinal nerve fiber layer or the macula with optical coherence tomography (OCT), are able to detect anatomical changes in glaucomatous eyes. Such changes may be observed before any functional damage. In this work, we built a generative deep learning model using the conditional GAN architecture to predict glaucoma progression over time. The patient{\textquoteright}s OCT scan is predicted from three or two prior measurements. The predicted images demonstrate high similarity with the ground truth images. In addition, our results suggest that OCT scans obtained from only two prior visits may actually be sufficient to predict the next OCT scan of the patient after six months.",

keywords = "CGAN, Generative models, Glaucoma progression, OCT",

author = "Hassan, {Osama N.} and Serhat Sahin and Vahid Mohammadzadeh and Xiaohe Yang and Navid Amini and Apoorva Mylavarapu and Jack Martinyan and Tae Hong and Golnoush Mahmoudinezhad and Daniel Rueckert and Kouros Nouri-Mahdavi and Fabien Scalzo",

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year = "2020",

doi = "10.1007/978-3-030-64559-5_61",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "761--772",

editor = "George Bebis and Zhaozheng Yin and Edward Kim and Jan Bender and Kartic Subr and Kwon, {Bum Chul} and Jian Zhao and Denis Kalkofen and George Baciu",

booktitle = "Advances in Visual Computing - 15th International Symposium, ISVC 2020, Proceedings",

}

Hassan, ON, Sahin, S, Mohammadzadeh, V, Yang, X, Amini, N, Mylavarapu, A, Martinyan, J, Hong, T, Mahmoudinezhad, G, Rueckert, D, Nouri-Mahdavi, K & Scalzo, F 2020, Conditional GAN for Prediction of Glaucoma Progression with Macular Optical Coherence Tomography. in G Bebis, Z Yin, E Kim, J Bender, K Subr, BC Kwon, J Zhao, D Kalkofen & G Baciu (eds), Advances in Visual Computing - 15th International Symposium, ISVC 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12510 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 761-772, 15th International Symposium on Visual Computing, ISVC 2020, San Diego, United States, 5/10/20. https://doi.org/10.1007/978-3-030-64559-5_61

Conditional GAN for Prediction of Glaucoma Progression with Macular Optical Coherence Tomography. / Hassan, Osama N.; Sahin, Serhat; Mohammadzadeh, Vahid et al.
Advances in Visual Computing - 15th International Symposium, ISVC 2020, Proceedings. ed. / George Bebis; Zhaozheng Yin; Edward Kim; Jan Bender; Kartic Subr; Bum Chul Kwon; Jian Zhao; Denis Kalkofen; George Baciu. Springer Science and Business Media Deutschland GmbH, 2020. p. 761-772 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12510 LNCS).

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

TY - GEN

T1 - Conditional GAN for Prediction of Glaucoma Progression with Macular Optical Coherence Tomography

AU - Hassan, Osama N.

AU - Sahin, Serhat

AU - Mohammadzadeh, Vahid

AU - Yang, Xiaohe

AU - Amini, Navid

AU - Mylavarapu, Apoorva

AU - Martinyan, Jack

AU - Hong, Tae

AU - Mahmoudinezhad, Golnoush

AU - Rueckert, Daniel

AU - Nouri-Mahdavi, Kouros

AU - Scalzo, Fabien

PY - 2020

Y1 - 2020

N2 - The estimation of glaucoma progression is a challenging task as the rate of disease progression varies among individuals in addition to other factors such as measurement variability and the lack of standardization in defining progression. Structural tests, such as thickness measurements of the retinal nerve fiber layer or the macula with optical coherence tomography (OCT), are able to detect anatomical changes in glaucomatous eyes. Such changes may be observed before any functional damage. In this work, we built a generative deep learning model using the conditional GAN architecture to predict glaucoma progression over time. The patient’s OCT scan is predicted from three or two prior measurements. The predicted images demonstrate high similarity with the ground truth images. In addition, our results suggest that OCT scans obtained from only two prior visits may actually be sufficient to predict the next OCT scan of the patient after six months.

AB - The estimation of glaucoma progression is a challenging task as the rate of disease progression varies among individuals in addition to other factors such as measurement variability and the lack of standardization in defining progression. Structural tests, such as thickness measurements of the retinal nerve fiber layer or the macula with optical coherence tomography (OCT), are able to detect anatomical changes in glaucomatous eyes. Such changes may be observed before any functional damage. In this work, we built a generative deep learning model using the conditional GAN architecture to predict glaucoma progression over time. The patient’s OCT scan is predicted from three or two prior measurements. The predicted images demonstrate high similarity with the ground truth images. In addition, our results suggest that OCT scans obtained from only two prior visits may actually be sufficient to predict the next OCT scan of the patient after six months.

KW - CGAN

KW - Generative models

KW - Glaucoma progression

KW - OCT

UR - http://www.scopus.com/inward/record.url?scp=85098163401&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-64559-5_61

DO - 10.1007/978-3-030-64559-5_61

M3 - Conference contribution

AN - SCOPUS:85098163401

SN - 9783030645588

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 761

EP - 772

BT - Advances in Visual Computing - 15th International Symposium, ISVC 2020, Proceedings

A2 - Bebis, George

A2 - Yin, Zhaozheng

A2 - Kim, Edward

A2 - Bender, Jan

A2 - Subr, Kartic

A2 - Kwon, Bum Chul

A2 - Zhao, Jian

A2 - Kalkofen, Denis

A2 - Baciu, George

PB - Springer Science and Business Media Deutschland GmbH

T2 - 15th International Symposium on Visual Computing, ISVC 2020

Y2 - 5 October 2020 through 7 October 2020

ER -

Hassan ON, Sahin S, Mohammadzadeh V, Yang X, Amini N, Mylavarapu A et al. Conditional GAN for Prediction of Glaucoma Progression with Macular Optical Coherence Tomography. In Bebis G, Yin Z, Kim E, Bender J, Subr K, Kwon BC, Zhao J, Kalkofen D, Baciu G, editors, Advances in Visual Computing - 15th International Symposium, ISVC 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 761-772. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-64559-5_61

Conditional GAN for Prediction of Glaucoma Progression with Macular Optical Coherence Tomography

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