Video-rate OCT image-enhancement using self-fusion neural network

Jose J. Rico-Jimenez; Dewei Hu; Eric M. Tang; Ipek Oguz; Yuankai K. Tao

doi:10.1117/12.2606626

7 March 2022 Video-rate OCT image-enhancement using self-fusion neural network

Jose J. Rico-Jimenez, Dewei Hu, Eric M. Tang, Ipek Oguz, Yuankai K. Tao

Author Affiliations +

Proceedings Volume PC11948, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI; PC119481D (2022) https://doi.org/10.1117/12.2606626
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

Ophthalmic OCT image-quality is highly variable and directly impacts clinical diagnosis of disease. Computational methods such as frame-averaging, filtering, deep-learning approaches are generally constrained by either extended imaging times when acquiring repeated-frames, over-smoothing and loss of features, or the need for extensive training sets. Self-fusion is a robust OCT image-enhancement method that overcomes these aforementioned limitations by averaging serial OCT frames weighted by their respective similarity. Here, we demonstrated video-rate self-fusion using a convolutional neural network. Our experimental results show a near doubling of OCT contrast-to-noise ratio at a frame-rate of ~22 fps when integrated with custom OCT acquisition software.

Conference Presentation

Citation Download Citation

Jose J. Rico-Jimenez, Dewei Hu, Eric M. Tang, Ipek Oguz, and Yuankai K. Tao "Video-rate OCT image-enhancement using self-fusion neural network", Proc. SPIE PC11948, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI, PC119481D (7 March 2022); https://doi.org/10.1117/12.2606626

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