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9 March 2020 Real-time retinal layer segmentation of OCT images: from graph cut to deep learning (Conference Presentation)
Svetlana Borkovkina, Worawee Janpongsri, Acner Camino, Marinko Sarunic, Yifan Jian
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Proceedings Volume 11228, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXIV; 1122822 (2020) https://doi.org/10.1117/12.2546490
Event: SPIE BiOS, 2020, San Francisco, California, United States
Abstract
Segmentation of the retinal layers in OCT images is the critical step in analyzing OCT volumetric data for diagnosis and monitoring of retinal disease progression. Real-time retinal layer segmentation has become increasingly desirable with the increasing OCT acquisition speed. In this work we explored methods to accelerate image processing method to segment retinal layers in OCT B-scan images including graph cut and deep learning. We demonstrated ~30-ms and ~3-ms segmentation of 7 retina layers per OCT B-scan with graph cut and deep learning respectively. The accelerated OCT B-scan segmentation was then integrated with our GPU OCT image acquisition software.
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Svetlana Borkovkina, Worawee Janpongsri, Acner Camino, Marinko Sarunic, and Yifan Jian "Real-time retinal layer segmentation of OCT images: from graph cut to deep learning (Conference Presentation)", Proc. SPIE 11228, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXIV, 1122822 (9 March 2020); https://doi.org/10.1117/12.2546490
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KEYWORDS
Image segmentation
Optical coherence tomography
Image processing
Image acquisition
Retina
Image resolution
Real time imaging
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