Abstract
Optical coherence tomography (OCT) is an important retinal imaging method since it is a non-invasive, high-resolution imaging technique and is able to reveal the fine structure within the human retina. It has applications for retinal as well as neurological disease characterization and diagnostics. The use of machine learning techniques for analyzing the retinal layers and lesions seen in OCT can greatly facilitate such diagnostics tasks. The use of deep learning (DL) methods principally using fully convolutional networks has recently resulted in significant progress in automated segmentation of optical coherence tomography. Recent work in that area is reviewed herein.
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Pekala, M., Joshi, N., Liu, T.Y.A., Bressler, N.M., Cabrera DeBuc, D., Burlina, P. (2019). OCT Segmentation via Deep Learning: A Review of Recent Work. In: Carneiro, G., You, S. (eds) Computer Vision – ACCV 2018 Workshops. ACCV 2018. Lecture Notes in Computer Science(), vol 11367. Springer, Cham. https://doi.org/10.1007/978-3-030-21074-8_27
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