Conditional GAN for Prediction of Glaucoma Progression with Macular Optical Coherence Tomography
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)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12510 LNCS
Pages Range: 761-772
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: San Diego, USA
ISBN: 9783030645588
DOI: 10.1007/978-3-030-64559-5_61
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.
Involved external institutions
University of California Los Angeles (UCLA)
United States (USA) (US)
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
UCLA Stein Eye Institute
United States (USA) (US)
California State University, Los Angeles
United States (USA) (US)
United States (USA) (US)
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
UCLA Stein Eye Institute
United States (USA) (US)
California State University, Los Angeles
United States (USA) (US)
How to cite
APA:
Hassan, O.N., Sahin, S., Mohammadzadeh, V., Yang, X., Amini, N., Mylavarapu, A.,... Scalzo, F. (2020). Conditional GAN for Prediction of Glaucoma Progression with Macular Optical Coherence Tomography. In George Bebis, Zhaozheng Yin, Edward Kim, Jan Bender, Kartic Subr, Bum Chul Kwon, Jian Zhao, Denis Kalkofen, George Baciu (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 761-772). San Diego, USA: Springer Science and Business Media Deutschland GmbH.
MLA:
Hassan, Osama N., et al. "Conditional GAN for Prediction of Glaucoma Progression with Macular Optical Coherence Tomography." Proceedings of the 15th International Symposium on Visual Computing, ISVC 2020, San Diego, USA 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. 761-772.
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