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Enhancing Portable OCT Image Quality via GANs for AI-Based Eye Disease Detection

Publication ,  Conference
Thakoor, KA; Carter, A; Song, G; Wax, A; Moussa, O; Chen, RWS; Hendon, C; Sajda, P
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2022

Optical coherence tomography (OCT) is widely used for detection of ophthalmic diseases, such as glaucoma, age-related macular degeneration (AMD), and diabetic retinopathy. Using a low-coherence-length light source, OCT is able to achieve high axial resolution in biological samples; this depth information is used by ophthalmologists to assess retinal structures and characterize disease states. However, OCT systems are often bulky and expensive, costing tens of thousands of dollars and weighing on the order of 50 pounds or more. Such constraints make it difficult for OCT to be accessible in low-resource settings. In the U.S. alone, only 15.3% of diabetic patients meet the recommendation of obtaining annual eye exams; the situation is even worse for minority/under-served populations. In this study, we focus on data acquired with a low-cost, portable OCT (p-OCT) device, characterized by lower resolution, scanning rate, and imaging depth than a commercial OCT system. We use generative adversarial networks (GANs) to enhance the quality of this p-OCT data and then assess the impact of this enhancement on downstream performance of artificial intelligence (AI) algorithms for AMD detection. Using GANs trained on simulated p-OCT data generated from paired commercial OCT data degraded with the point spread function (PSF) of the p-OCT device, we observe improved AI performance on p-OCT data after single-image super-resolution. We also achieve denoising after image-to-image translation. By exhibiting proof-of-principle AI-based AMD detection even on low-quality p-OCT data, this study stimulates future work toward low-cost, portable imaging+AI systems for eye disease detection.

Duke Scholars

Published In

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

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783031185229

Publication Date

January 1, 2022

Volume

13573 LNCS

Start / End Page

155 / 167

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Thakoor, K. A., Carter, A., Song, G., Wax, A., Moussa, O., Chen, R. W. S., … Sajda, P. (2022). Enhancing Portable OCT Image Quality via GANs for AI-Based Eye Disease Detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13573 LNCS, pp. 155–167). https://doi.org/10.1007/978-3-031-18523-6_15
Thakoor, K. A., A. Carter, G. Song, A. Wax, O. Moussa, R. W. S. Chen, C. Hendon, and P. Sajda. “Enhancing Portable OCT Image Quality via GANs for AI-Based Eye Disease Detection.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13573 LNCS:155–67, 2022. https://doi.org/10.1007/978-3-031-18523-6_15.
Thakoor KA, Carter A, Song G, Wax A, Moussa O, Chen RWS, et al. Enhancing Portable OCT Image Quality via GANs for AI-Based Eye Disease Detection. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2022. p. 155–67.
Thakoor, K. A., et al. “Enhancing Portable OCT Image Quality via GANs for AI-Based Eye Disease Detection.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13573 LNCS, 2022, pp. 155–67. Scopus, doi:10.1007/978-3-031-18523-6_15.
Thakoor KA, Carter A, Song G, Wax A, Moussa O, Chen RWS, Hendon C, Sajda P. Enhancing Portable OCT Image Quality via GANs for AI-Based Eye Disease Detection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2022. p. 155–167.
Journal cover image

Published In

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

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783031185229

Publication Date

January 1, 2022

Volume

13573 LNCS

Start / End Page

155 / 167

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences