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

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Mariottoni, EB; Jammal, AA; Urata, CN; Berchuck, SI; Thompson, AC; Estrela, T; Medeiros, FA

Published Date

  • January 15, 2020

Published In

Volume / Issue

  • 10 / 1

Start / End Page

  • 402 -

PubMed ID

  • 31941958

Pubmed Central ID

  • PMC6962147

Electronic International Standard Serial Number (EISSN)

  • 2045-2322

Digital Object Identifier (DOI)

  • 10.1038/s41598-019-57196-y

Language

  • eng

Conference Location

  • England