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Quantification of Retinal Nerve Fibre Layer Thickness on Optical Coherence Tomography with a Deep Learning Segmentation-Free Approach.

Publication ,  Journal Article
Mariottoni, EB; Jammal, AA; Urata, CN; Berchuck, SI; Thompson, AC; Estrela, T; Medeiros, FA
Published in: Sci Rep
January 15, 2020

This study describes a segmentation-free deep learning (DL) algorithm for measuring retinal nerve fibre layer (RNFL) thickness on spectral-domain optical coherence tomography (SDOCT). The study included 25,285 B-scans from 1,338 eyes of 706 subjects. Training was done to predict RNFL thickness from raw unsegmented scans using conventional RNFL thickness measurements from good quality images as targets, forcing the DL algorithm to learn its own representation of RNFL. The algorithm was tested in three different sets: (1) images without segmentation errors or artefacts, (2) low-quality images with segmentation errors, and (3) images with other artefacts. In test set 1, segmentation-free RNFL predictions were highly correlated with conventional RNFL thickness (r = 0.983, P < 0.001). In test set 2, segmentation-free predictions had higher correlation with the best available estimate (tests with good quality taken in the same date) compared to those from the conventional algorithm (r = 0.972 vs. r = 0.829, respectively; P < 0.001). Segmentation-free predictions were also better in test set 3 (r = 0.940 vs. r = 0.640, P < 0.001). In conclusion, a novel segmentation-free algorithm to extract RNFL thickness performed similarly to the conventional method in good quality images and better in images with errors or other artefacts.

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Published In

Sci Rep

DOI

EISSN

2045-2322

Publication Date

January 15, 2020

Volume

10

Issue

1

Start / End Page

402

Location

England

Related Subject Headings

  • Visual Fields
  • Tomography, Optical Coherence
  • Retinal Ganglion Cells
  • Nerve Fibers
  • Middle Aged
  • Male
  • Image Processing, Computer-Assisted
  • Humans
  • Glaucoma
  • Female
 

Citation

APA
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MLA
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Mariottoni, E. B., Jammal, A. A., Urata, C. N., Berchuck, S. I., Thompson, A. C., Estrela, T., & Medeiros, F. A. (2020). Quantification of Retinal Nerve Fibre Layer Thickness on Optical Coherence Tomography with a Deep Learning Segmentation-Free Approach. Sci Rep, 10(1), 402. https://doi.org/10.1038/s41598-019-57196-y
Mariottoni, Eduardo B., Alessandro A. Jammal, Carla N. Urata, Samuel I. Berchuck, Atalie C. Thompson, Tais Estrela, and Felipe A. Medeiros. “Quantification of Retinal Nerve Fibre Layer Thickness on Optical Coherence Tomography with a Deep Learning Segmentation-Free Approach.Sci Rep 10, no. 1 (January 15, 2020): 402. https://doi.org/10.1038/s41598-019-57196-y.
Mariottoni EB, Jammal AA, Urata CN, Berchuck SI, Thompson AC, Estrela T, et al. Quantification of Retinal Nerve Fibre Layer Thickness on Optical Coherence Tomography with a Deep Learning Segmentation-Free Approach. Sci Rep. 2020 Jan 15;10(1):402.
Mariottoni, Eduardo B., et al. “Quantification of Retinal Nerve Fibre Layer Thickness on Optical Coherence Tomography with a Deep Learning Segmentation-Free Approach.Sci Rep, vol. 10, no. 1, Jan. 2020, p. 402. Pubmed, doi:10.1038/s41598-019-57196-y.
Mariottoni EB, Jammal AA, Urata CN, Berchuck SI, Thompson AC, Estrela T, Medeiros FA. Quantification of Retinal Nerve Fibre Layer Thickness on Optical Coherence Tomography with a Deep Learning Segmentation-Free Approach. Sci Rep. 2020 Jan 15;10(1):402.

Published In

Sci Rep

DOI

EISSN

2045-2322

Publication Date

January 15, 2020

Volume

10

Issue

1

Start / End Page

402

Location

England

Related Subject Headings

  • Visual Fields
  • Tomography, Optical Coherence
  • Retinal Ganglion Cells
  • Nerve Fibers
  • Middle Aged
  • Male
  • Image Processing, Computer-Assisted
  • Humans
  • Glaucoma
  • Female