Semiconductor Optoelectronics, Volume. 41, Issue 5, 605(2020)

A Review of Retina OCT B-scan Image Segmentation Methods

YUAN Kun and HUO Li
Author Affiliations
  • [in Chinese]
  • show less
    References(24)

    [1] [1] Huang D, Swanson E A, Lin C P, et al. Optical coherence tomography[J]. Science, 1991, 254(5035): 1178-1181.

    [2] [2] Chang S, Mao Y, Flueraru C, et al. Optical coherence tomography: technology and applications[C]// Proc. 2008 Inter. Conf. on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments. Inter. Society for Optics and Photonics, 2009.

    [3] [3] DeBuc D C. A review of algorithms for segmentation of retinal image data using optical coherence tomography[J]. Image Segmentation, 2011, 1: 15-54.

    [4] [4] Kafieh R, Rabbani H, Kermani S. A review of algorithms for segmentation of optical coherence tomography from retina[J]. J. of Medical Signals and Sensors, 2013, 3(1): 45.

    [5] [5] Mitsui T. Dynamic range of optical reflectometry with spectral interferometry[J]. Japanese J. of Appl. Phys., 1999, 38(10R): 6133.

    [6] [6] Leitgeb R, Hitzenberger C K, Fercher A F. Performance of Fourier domain vs. time domain optical coherence tomography[J]. Opt. Express, 2003, 11(8): 889-894.

    [7] [7] De Boer J F, Cense B, Park B H, et al. Improved signal-to-noise ratio in spectral-domain compared with time-domain optical coherence tomography[J]. Opt. Lett., 2003, 28(21): 2067-2069.

    [8] [8] Wojtkowski M, Leitgeb R, Kowalczyk A, et al. In vivo human retinal imaging by Fourier domain optical coherence tomography[J]. J. of Biomedical Optics, 2002, 7(3): 457-463.

    [9] [9] Cense B, Nassif N A, Chen T C, et al. Ultrahigh-resolution high-speed retinal imaging using spectral-domain optical coherence tomography[J]. Opt. Express, 2004, 12(11): 2435-2447.

    [10] [10] Remington L A, Goodwin D. Clinical Anatomy of The Visual System E-Book[M]. UK: Elsevier Health Sciences, 2011.

    [11] [11] Drexler W. Ultrahigh-resolution optical coherence tomography[J]. J. of Biomedical Optics, 2004, 9(1): 47-75.

    [12] [12] Fernandez D C, Villate N, Puliafito C A, et al. Comparing total macular volume changes measured by optical coherence tomography with retinal lesion volume estimated by active contours[J]. Investigative Ophthalmology & Visual Science, 2004, 45(13): 3072-3072.

    [13] [13] Baroni M, Fortunato P, La Torre A. Towards quantitative analysis of retinal features in optical coherence tomography[J]. Medical Engin. & Phys., 2007, 29(4): 432-441.

    [14] [14] Tan O, Li G, Lu A T H, et al. Mapping of macular substructures with optical coherence tomography for glaucoma diagnosis[J]. Ophthalmology, 2008, 115(6): 949-956.

    [15] [15] Kajic V, Povazay B, Hermann B, et al. Robust segmentation of intraretinal layers in the normal human fovea using a novel statistical model based on texture and shape analysis[J]. Opt. Express, 2010, 18(14): 14730-14744.

    [16] [16] Mayer M A, Hornegger J, Mardin C Y, et al. Retinal nerve fiber layer segmentation on FD-OCT scans of normal subjects and glaucoma patients[J]. Biomedical Optics Express, 2010, 1(5): 1358-1383.

    [17] [17] Chiu S J, Li X T, Nicholas P, et al. Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation[J]. Opt. Express, 2010, 18(18): 19413-19428.

    [18] [18] Ghorbel I, Rossant F, Bloch I, et al. Automated segmentation of macular layers in OCT images and quantitative evaluation of performances[J]. Pattern Recognition, 2011, 44(8): 1590-1603.

    [19] [19] McDonough K, Kolmanovsky I, Glybina I V. A neural network approach to retinal layer boundary identification from optical coherence tomography images[C]// Proc. 2015 IEEE Conf. on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2015: 1-8.

    [20] [20] Shah A, Zhou L, Abramoff M D, et al. Multiple surface segmentation using convolution neural nets: application to retinal layer segmentation in OCT images[J]. Biomedical Optics Express, 2018, 9(9): 4509-4526.

    [21] [21] Vermeer K A, Van der Schoot J, Lemij H G, et al. Automated segmentation by pixel classification of retinal layers in ophthalmic OCT images[J]. Biomedical Optics Express, 2011, 2(6): 1743-1756.

    [22] [22] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[C]// Proc. Inter. Conf. on Medical Image Computing and Computer-Assisted Intervention, 2015: 234-241.

    [23] [23] Roy A G, Conjeti S, Karri S P K, et al. ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks[J]. Biomedical Optics Express, 2017, 8(8): 3627-3642.

    [24] [24] McDonough K, Kolmanovsky I, Glybina I V. A neural network approach to retinal layer boundary identification from optical coherence tomography images[C]// Proc. 2015 IEEE Conf. on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2015: 1-8.

    Tools

    Get Citation

    Copy Citation Text

    YUAN Kun, HUO Li. A Review of Retina OCT B-scan Image Segmentation Methods[J]. Semiconductor Optoelectronics, 2020, 41(5): 605

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: Apr. 21, 2020

    Accepted: --

    Published Online: Jan. 19, 2021

    The Author Email:

    DOI:10.16818/j.issn1001-5868.2020.05.001

    Topics