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OCT Segmentation via Deep Learning: A Review of Recent Work

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Computer Vision – ACCV 2018 Workshops (ACCV 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11367))

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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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Author information

Authors and Affiliations

  1. Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA

    M. Pekala, N. Joshi & P. Burlina

  2. Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    T. Y. Alvin Liu & N. M. Bressler

  3. Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA

    D. Cabrera DeBuc

Authors
  1. M. Pekala
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  2. N. Joshi
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  3. T. Y. Alvin Liu
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  4. N. M. Bressler
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  5. D. Cabrera DeBuc
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  6. P. Burlina
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Corresponding author

Correspondence to P. Burlina .

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Editors and Affiliations

  1. School of Computer Science, University of Adelaide, Adelaide, Australia

    Gustavo Carneiro

  2. Data61, Commonwealth Scientific and Industrial Research Organization, Canberra, Australia

    Shaodi You

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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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  • DOI: https://doi.org/10.1007/978-3-030-21074-8_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-21073-1

  • Online ISBN: 978-3-030-21074-8

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