An AI-based algorithmic system that predicts missing A-scans in cross-sectional retinal images

Omer Faruk Dinc; Berfin Arlı; Serhat Tozburun

doi:10.1117/12.2671958

11 August 2023 An AI-based algorithmic system that predicts missing A-scans in cross-sectional retinal images

Omer Faruk Dinc, Berfin Arlı, Serhat Tozburun

Proceedings Volume 12632, Optical Coherence Imaging Techniques and Imaging in Scattering Media V; 126321W (2023) https://doi.org/10.1117/12.2671958
Event: European Conferences on Biomedical Optics, 2023, Munich, Germany

Abstract

In this study, we present an artificial intelligence based algorithmic system that predicts missing A-scans of the edited OCT image by padding the A scan with zero. The developed artificial intelligence algorithmic system consists of two networks: convolutional neural network and generative adversarial network. The system theoretically suggests that skipping one-third of sequential A-scans and predicting the missing A-scan can increase the image acquisition rate by at least 33%. The structural similarity index measurement of the test data reaches an average of 82% between the ground truth images and the images predicted from the developed system. The mean squared error also is to 0.2%.

Citation Download Citation

Omer Faruk Dinc, Berfin Arlı, and Serhat Tozburun "An AI-based algorithmic system that predicts missing A-scans in cross-sectional retinal images", Proc. SPIE 12632, Optical Coherence Imaging Techniques and Imaging in Scattering Media V, 126321W (11 August 2023); https://doi.org/10.1117/12.2671958

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