June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
Automatic detection of the foveal center in optical coherence tomography
Author Affiliations & Notes
  • Bart Liefers
    Diagnostic Image Analysis Group, Radboudumc, Nijmegen, Netherlands
  • Freerk G Venhuizen
    Diagnostic Image Analysis Group, Radboudumc, Nijmegen, Netherlands
  • Vivian Schreur
    Ophthalmology, Radboudumc, Nijmegen, Netherlands
  • Bram van Ginneken
    Diagnostic Image Analysis Group, Radboudumc, Nijmegen, Netherlands
  • Carel C B Hoyng
    Ophthalmology, Radboudumc, Nijmegen, Netherlands
  • Thomas Theelen
    Ophthalmology, Radboudumc, Nijmegen, Netherlands
  • Clara I Sanchez
    Diagnostic Image Analysis Group, Radboudumc, Nijmegen, Netherlands
  • Footnotes
    Commercial Relationships   Bart Liefers, None; Freerk Venhuizen, None; Vivian Schreur, None; Bram van Ginneken, None; Carel Hoyng, None; Thomas Theelen, None; Clara Sanchez, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 670. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Bart Liefers, Freerk G Venhuizen, Vivian Schreur, Bram van Ginneken, Carel C B Hoyng, Thomas Theelen, Clara I Sanchez; Automatic detection of the foveal center in optical coherence tomography. Invest. Ophthalmol. Vis. Sci. 2017;58(8):670.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Purpose : To automatically detect the foveal center in optical coherence tomography (OCT) scans in order to obtain an accurate and reliable reference for the assessment of various structural biomarkers, even in the presence of large abnormalities and across different scanning protocols.

Methods : 1784 OCT scans were used for the development of the proposed automatic method: 1744 scans from the European Genetic Database (EUGENDA) acquired with a Heidelberg Spectralis HRA+OCT 1 scanner and 40 scans from a publicly available dataset [1] acquired with a Bioptigen scanner. Two independent sets, with different levels of age-related macular degeneration (AMD) were drawn from the same databases for evaluation: 100 scans from EUGENDA (Set A, 25 control patients and 25 for each of the AMD severity levels early, intermediate and advanced) and 100 scans from [1] (Set B, 50 control, 50 AMD).
A fully convolutional neural network based on stacked layers of dilated convolutions was trained to classify each pixel in a B-scan by assigning a probability of belonging to the fovea. The network was applied to every B-scan in the OCT volume, and the final foveal center was defined as the pixel with maximum assigned probability. An initial network was trained on the 1744 training scans from EUGENDA and optimized with the 40 training scans acquired with the Bioptigen scanner, to specialize for different levels of noise and contrast.

For all scans manual annotations were available as reference for evaluation. The foveal center was considered correctly identified if the distance between the prediction and the reference was less than the foveal radius, i.e. 750 μm.

Results : The foveal center was correctly detected in 95 OCT scans in Set A (24 control, 24 early, 25 intermediate, 22 advanced). The mean distance error was 63.7 μm with 81 detections inside a radius of 175 μm (the foveola) and 70 inside a radius of 75 μm (the umbo). In Set B, the foveal center was correctly identified in 96 OCT scans (49 control, 47 AMD). The mean distance error was 88.6 μm with 82 detections inside the foveola and 61 inside the umbo.

Conclusions : The proposed automatic method performed accurately for both healthy retinas and retinas affected by AMD. The method can be applied successfully to scans from different vendors, thus providing a reliable reference location for the assessment of structural biomarkers in OCT.

[1] http://people.duke.edu/~sf59/RPEDC_Ophth_2013_dataset.htm

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

 

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×