June 2013
Volume 54, Issue 15
Free
ARVO Annual Meeting Abstract  |   June 2013
Comparison of coupled level sets and graph cuts for retinal layer segmentation in optical coherence tomography
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
  • Marvin Ostermann
    Quantitative Imaging Group, Faculty of Applied Science, Delft University of Technology, Delft, Netherlands
  • Gijs Thepass
    Rotterdam Ophthalmic Institute, Rotterdam Eye Hospital, Rotterdam, Netherlands
  • Hans Lemij
    Glaucoma Service, Rotterdam Eye Hospital, Rotterdam, Netherlands
  • Koenraad Vermeer
    Rotterdam Ophthalmic Institute, Rotterdam Eye Hospital, Rotterdam, Netherlands
  • Lucas van Vliet
    Quantitative Imaging Group, Faculty of Applied Science, Delft University of Technology, Delft, Netherlands
  • Footnotes
    Commercial Relationships Jelena Novosel, Heidelberg Engineering (F); Marvin Ostermann, None; Gijs Thepass, None; Hans Lemij, Carl Zeiss Meditec (C); Koenraad Vermeer, Heidelberg Engineering (F), General Hospital Corporation (P); Lucas van Vliet, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 1462. doi:
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      Jelena Novosel, Marvin Ostermann, Gijs Thepass, Hans Lemij, Koenraad Vermeer, Lucas van Vliet; Comparison of coupled level sets and graph cuts for retinal layer segmentation in optical coherence tomography. Invest. Ophthalmol. Vis. Sci. 2013;54(15):1462.

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

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Abstract
 
Purpose
 

The accuracy and reproducibility of two retinal layer segmentation methods were evaluated. Graph cuts (GC) and a novel level set (LS) approach were applied to peripapillary OCT images, which were first converted to attenuation coefficients. As an optical tissue property, the attenuation coefficient is not affected by common imaging artefacts such as shading.

 
Methods
 

Segmentation methods detect interfaces between layers principally based on intensity information. A novel LS method exploits anatomical knowledge about the retina and incorporates it via LS coupling, thereby simultaneously detecting interfaces. A GC approach was adapted to favour layered structures. Starting from the outer retina, it iteratively detects retinal layers. One eye for each of 24 normal subjects was imaged with a Spectralis OCT system. 10 eyes were used for training of the algorithms, the other 14 eyes were used to assess the accuracy. 6 eyes were imaged again on the next day to evaluate the reproducibility. One B-scan of every scan was manually segmented. Three interfaces were considered: the vitreous-RNFL interface, the RNFL-GCL interface and the IPL-INL interface and thicknesses of two layers: the RNFL and the GCC (the RNFL, GCL and IPL). Errors used were the root mean square error (RMS), mean absolute deviation (MAD) and Dice coefficient; blood vessels were excluded from evaluation.

 
Results
 

An example of the segmentation results on the attenuation coefficient image of a normal and glaucoma eye is shown in figure 1. All evaluation results are listed in figure 2. The accuracy of LS expressed by MAD is on average 1.5 µm better than the GC. The reproducibility of automated methods has on average 1 µm smaller MAD than the reproducibility of manual annotations. The reproducibility of LS is on average 0.3 µm (MAD) better than the reproducibility of GC. The accuracy of both methods is within 2 µm of the reproducibility of manual annotations.

 
Conclusions
 

Both methods are capable of segmenting OCT data, but our LS approach shows better accuracy and reproducibility. Automated methods are more consistent than manual annotations. Accuracy of both methods and intra-observer reproducibility of manual annotations are similar suggesting that the accuracy of the automatic segmentation is at least as good as the manual annotations.

 
 
Segmentation results on a normal and glaucoma eye
 
Segmentation results on a normal and glaucoma eye
 
 
Accuracy and reproducibility evaluation
 
Accuracy and reproducibility evaluation
 
Keywords: 549 image processing • 552 imaging methods (CT, FA, ICG, MRI, OCT, RTA, SLO, ultrasound) • 551 imaging/image analysis: non-clinical  
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