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2019

OCT Segmentation via Deep Learning: A Review of Recent Work


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.

Citation

article: Pekala_2019 doi: 10.1016/j.compbiomed.2019.103445 url: https://doi.org/10.1016/j.compbiomed.2019.103445 year: 2019 month: nov publisher: Elsevier BV volume: 114 pages: 103445 author: Pekala M. and Joshi N. and Liu T.Y. Alvin and Bressler N.M. and DeBuc D. Cabrera and Burlina P. title: Deep learning based retinal OCT segmentation journal: Computers in Biology and Medicine

Citation

article: Pekala_2019 doi: 10.1016/j.compbiomed.2019.103445 url: https://doi.org/10.1016/j.compbiomed.2019.103445 year: 2019 month: nov publisher: Elsevier BV volume: 114 pages: 103445 author: Pekala M. and Joshi N. and Liu T.Y. Alvin and Bressler N.M. and DeBuc D. Cabrera and Burlina P. title: Deep learning based retinal OCT segmentation journal: Computers in Biology and Medicine