June 2015
Volume 56, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2015
Automatic quantification of geographic atrophy in autofluorescence images of Stargardt patients
Author Affiliations & Notes
  • Clara I Sanchez
    Diagnostic Image Analysis Group, RadboudUMC, Nijmegen, Netherlands
    Ophthalmology, RadboudUMC, Nijmegen, Netherlands
  • Stanley Lambertus
    Ophthalmology, RadboudUMC, Nijmegen, Netherlands
  • Bart Bloemen
    Diagnostic Image Analysis Group, RadboudUMC, Nijmegen, Netherlands
  • Nathalie Bax
    Ophthalmology, RadboudUMC, Nijmegen, Netherlands
  • Freerk Gerhard Venhuizen
    Diagnostic Image Analysis Group, RadboudUMC, Nijmegen, Netherlands
  • Mark J J P Van Grinsven
    Diagnostic Image Analysis Group, RadboudUMC, Nijmegen, Netherlands
  • Bram van Ginneken
    Diagnostic Image Analysis Group, RadboudUMC, Nijmegen, Netherlands
  • Thomas Theelen
    Ophthalmology, RadboudUMC, Nijmegen, Netherlands
  • Carel C B Hoyng
    Ophthalmology, RadboudUMC, Nijmegen, Netherlands
  • Footnotes
    Commercial Relationships Clara Sanchez, None; Stanley Lambertus, None; Bart Bloemen, None; Nathalie Bax, None; Freerk Venhuizen, None; Mark Van Grinsven, None; Bram Ginneken, None; Thomas Theelen, None; Carel Hoyng, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 5258. doi:
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    • Get Citation

      Clara I Sanchez, Stanley Lambertus, Bart Bloemen, Nathalie Bax, Freerk Gerhard Venhuizen, Mark J J P Van Grinsven, Bram van Ginneken, Thomas Theelen, Carel C B Hoyng; Automatic quantification of geographic atrophy in autofluorescence images of Stargardt patients. Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):5258.

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

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Abstract
 
Purpose
 

To evaluate an observer-independent image analysis algorithm that automatically quantifies the area of geographic atrophy in fundus autofluorescence images of Stargardt patients.

 
Methods
 

Fundus autofluorescence images of 20 eyes of 20 Stargardt patients with presence of one delineated or patchy atrophy region in the macular area were selected. An image analysis algorithm was developed to automatically segment the area of atrophy starting from an arbitrarily selected seed point inside the atrophy region. The method was based on a combination of region growing algorithm and a dynamic, user-independent threshold selection procedure using Otsu thresholding. In order to assess the performance obtained by the proposed algorithm, manual annotations were made by an experienced human grader. The grader manually delineated the atrophy areas on the same set of images using dedicated software developed for this task.

 
Results
 

A high correlation was observed between the manual area measurements and the automatically quantified values obtained by the proposed algorithm, with a mean intra-class correlation coefficient (ICC) value larger than 0.89. In addition, the quantification time was reduced substantially by a factor of 27 compared to manual assessment. The output of the software was also shown to be independent of the user input and highly reproducible, with an ICC value larger than 0.99 between two executions of the algorithm at different time points and with different seed points.

 
Conclusions
 

An image analysis algorithm for automatic quantification of geographic atrophy in autofluorescence images of Stargardt patients was developed. The proposed algorithm allows for precise, reproducible and fast quantification of the atrophy area, providing an accurate procedure to measure disease progression and assess potential therapies in large dataset analyses independent of human observers.  

 
Figure 1. Original fundus autofluorescence image of a Stargardt patient (left), manual annotation of the atrophy region (center) and automatically quantified atrophy region obtained by the proposed algorithm (right)
 
Figure 1. Original fundus autofluorescence image of a Stargardt patient (left), manual annotation of the atrophy region (center) and automatically quantified atrophy region obtained by the proposed algorithm (right)

 
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