April 2014
Volume 55, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2014
Loosely coupled level sets for 3D retinal layer segmentation in optical coherence tomography scans of healthy and glaucoma subjects
Author Affiliations & Notes
  • Jelena Novosel
    Rotterdam Ophthalmic Institute, Rotterdam Eye Hospital, Rotterdam, Netherlands
    Quantitative Imaging Group, Faculty of Applied Science, Delft University of Technology, Delft, Netherlands
  • Gijs Thepass
    Rotterdam Ophthalmic Institute, Rotterdam Eye Hospital, Rotterdam, Netherlands
  • Hans G Lemij
    Glaucoma Service, Rotterdam Eye Hospital, Rotterdam, Netherlands
  • Johannes F de Boer
    Department of Physics and Astronomy, VU Amsterdam, Amsterdam, Netherlands
  • Koenraad Arndt Vermeer
    Rotterdam Ophthalmic Institute, Rotterdam Eye Hospital, Rotterdam, Netherlands
  • Lucas J. van Vliet
    Quantitative Imaging Group, Faculty of Applied Science, Delft University of Technology, Delft, Netherlands
  • Footnotes
    Commercial Relationships Jelena Novosel, Heidelberg Engineering: (F); Gijs Thepass, Heidelberg Engineering (F); Hans Lemij, Canon (F), Carl Zeiss Meditec (F), Heidelberg Engineering (F), Optovue (F); Johannes de Boer, VU university (P); Koenraad Vermeer, General Hospital Corporation (P), Heidelberg Engineering (F); Lucas J. van Vliet, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 4792. doi:https://doi.org/
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      Jelena Novosel, Gijs Thepass, Hans G Lemij, Johannes F de Boer, Koenraad Arndt Vermeer, Lucas J. van Vliet; Loosely coupled level sets for 3D retinal layer segmentation in optical coherence tomography scans of healthy and glaucoma subjects. Invest. Ophthalmol. Vis. Sci. 2014;55(13):4792. doi: https://doi.org/.

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

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Abstract
 
Purpose
 

To present and evaluate a novel level set (LS) approach for retinal layer segmentation in peripapillary OCT volumes. The accuracy and reproducibility were evaluated on normal and glaucomatous eyes

 
Methods
 

Interfaces between layers were detected simultaneously by using a novel LS coupling approach on attenuation coefficients (AC) derived from OCT volume scans. Coupling of the interfaces exploits the order of the layers in the retina and thus ensures anatomically correct segmentation results. This anatomical knowledge was incorporated in a probabilistic framework and combined with image data and prior knowledge on the layers’ AC and thicknesses. To assess the accuracy, one eye of 20 healthy subjects and 10 glaucoma patients was imaged with a Spectralis OCT system (Heidelberg Engineering, Germany). 9 healthy subjects were imaged again after a few minutes to evaluate the reproducibility. The scan protocol combined 193 B-scans composed of 512 A-lines of 496 pixels into one volume, with 5 times averaging. One randomly selected B-scan of every volume was manually segmented by a medical doctor. Six interfaces were considered: vitreous-RNFL, RNFL-GCL, IPL-INL, INL-OPL, OPL-ONL and the IS ellipsoid’s posterior boundary. Differences between segmentations were expressed as the root mean square error (RMSE) and mean absolute deviation (MAD).

 
Results
 

Examples of the segmentation on a B-scan of a normal and glaucoma eye are shown in Fig. 1. Evaluation results are listed in Tab. 1. The MAD for accuracy of the segmentation varied between 3.3-7.6 µm and 3.5-9.7 µm for healthy and glaucoma subjects, respectively. The reproducibility of manual annotations showed an MAD of 3.1-7.9 µm, while the automatic method (AM) showed an MAD of 2.5-4.6 µm.

 
Conclusions
 

A novel 3D segmentation method, which simultaneously detects multiple interfaces via a new coupling approach, was presented. The AM achieved a high accuracy in segmenting the volumes of healthy and glaucoma subjects. The reproducibility of the AM is better than manual annotations. The accuracy of the AM and the intra-observer reproducibility are similar suggesting that the accuracy of the AM is as good as the manual annotations.

 
 
Tab. 1. Accuracy and reproducibility evaluation results
 
Tab. 1. Accuracy and reproducibility evaluation results
 
 
Fig. 1. Segmentation results and resulting thickness maps for a normal and glaucoma eye. The A-lines passing through the vasculature were excluded
 
Fig. 1. Segmentation results and resulting thickness maps for a normal and glaucoma eye. The A-lines passing through the vasculature were excluded
 
Keywords: 549 image processing • 551 imaging/image analysis: non-clinical • 552 imaging methods (CT, FA, ICG, MRI, OCT, RTA, SLO, ultrasound)  
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