June 2015
Volume 56, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2015
Disease Modelling & Prediction: Automated Fovea Detection as a Key Registration Landmark for Construction of a Population Reference Frame
Author Affiliations & Notes
  • Jing Wu
    Christian Doppler Laboratory for Ophthalmic Image Analysis, Vienna Reading Center, Department of Ophthalmology, Medical University of Vienna, Vienna, Austria
  • Sebastian M Waldstein
    Christian Doppler Laboratory for Ophthalmic Image Analysis, Vienna Reading Center, Department of Ophthalmology, Medical University of Vienna, Vienna, Austria
  • Bianca S. Gerendas
    Christian Doppler Laboratory for Ophthalmic Image Analysis, Vienna Reading Center, Department of Ophthalmology, Medical University of Vienna, Vienna, Austria
  • Roland Leitner
    Christian Doppler Laboratory for Ophthalmic Image Analysis, Vienna Reading Center, Department of Ophthalmology, Medical University of Vienna, Vienna, Austria
  • Sinziana Birta
    Christian Doppler Laboratory for Ophthalmic Image Analysis, Vienna Reading Center, Department of Ophthalmology, Medical University of Vienna, Vienna, Austria
  • Georg Langs
    Christian Doppler Laboratory for Ophthalmic Image Analysis, Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
  • Christian Simader
    Christian Doppler Laboratory for Ophthalmic Image Analysis, Vienna Reading Center, Department of Ophthalmology, Medical University of Vienna, Vienna, Austria
  • Ursula Schmidt-Erfurth
    Christian Doppler Laboratory for Ophthalmic Image Analysis, Vienna Reading Center, Department of Ophthalmology, Medical University of Vienna, Vienna, Austria
  • Footnotes
    Commercial Relationships Jing Wu, None; Sebastian Waldstein, None; Bianca Gerendas, None; Roland Leitner, None; Sinziana Birta, None; Georg Langs, None; Christian Simader, None; Ursula Schmidt-Erfurth, Alcon (C), Bayer (C), Boehringer Ingelheim (C), Novartis (C)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 5917. doi:
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    • Get Citation

      Jing Wu, Sebastian M Waldstein, Bianca S. Gerendas, Roland Leitner, Sinziana Birta, Georg Langs, Christian Simader, Ursula Schmidt-Erfurth; Disease Modelling & Prediction: Automated Fovea Detection as a Key Registration Landmark for Construction of a Population Reference Frame. Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):5917.

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

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Abstract
 
Purpose
 

To automatically compute the foveal position in multi-vendor retinal spectral domain optical coherence tomography (SD-OCT) scans of patients with exudative macular disease (neovascular age-related macular degeneration (nAMD), diabetic macular edema and retinal vein occlusion (RVO)), used as key landmarks in the construction of a population reference frame for cross-patient spatio-temporal and group-wise disease modelling and prediction.

 
Methods
 

Initially, preprocessing is performed on each OCT scan: Z dimension motion correction, denoising by block matching collaborative filtering, and graph cut segmentation to delineate the internal limiting membrane (ILM) and cysts.<br /> First, the fovea type is distinguished. Combining the segmented contiguous ILM segments generates a 3D model of the probable fovea region (Fig 1.), from which 3 distinct fovea types are examined: 1) normal foveal depression (NFD), seen as a prominent depression spanning several contiguous B-scans (Fig 1.a); 2) minor foveal depression (MFD) which features a smaller depression elevated by asymmetric retinal edema and the presence of cysts (Fig 1.b); 3) absent foveal depression type (AFD) where no depression is seen due to retinal edema by cysts (Fig 1.c).<br /> Fovea position computation for NFD (Fig 2.a) identifies the centroid of all zero thickness positions between the ILM and RNFL surfaces in the masked region. For MFD & AFD (Fig 2.b,c), pairwise distance comparison between the ILM and cyst boundary point sets is performed. From the resulting closest spatial co-ordinate pair, the position xfovea in the B-scan plane is taken as xILM, yfovea is the current B-scan and zfovea is the corresponding position on the ILM surface zILM.

 
Results
 

Results from three disease groups each with 100 SD-OCT scans present mean (±SD) absolute distances between automated fovea detection results and manual annotated fovea for nAMD, branch RVO and central RVO to be 176.5±156.8 µm , 159.5±127.0 µm, and 165.0±143.8 µm respectively.

 
Conclusions
 

The presented method automatically and accurately computes the fovea position, the key landmark for creating a population reference frame, in diseased scans from “big data”. Thus this allows patient scans from different time points, vendors and modalities to be analysed in a common reference frame facilitating further advanced clinical analysis, disease modelling and prediction.  

 

 
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