September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
Graph-based fluid segmentation from OCT images
Author Affiliations & Notes
  • Ipek Oguz
    Ophthalmology, University of Iowa, Iowa City, Iowa, United States
  • Li Zhang
    Ophthalmology, University of Iowa, Iowa City, Iowa, United States
  • Andreas Wahle
    Ophthalmology, University of Iowa, Iowa City, Iowa, United States
  • Milan Sonka
    Ophthalmology, University of Iowa, Iowa City, Iowa, United States
  • Michael David Abramoff
    Ophthalmology, University of Iowa, Iowa City, Iowa, United States
  • Footnotes
    Commercial Relationships   Ipek Oguz, None; Li Zhang, None; Andreas Wahle, None; Milan Sonka, University of Iowa (P); Michael Abramoff, IDx LLC (C), IDx LLC (I), University of Iowa (P)
  • Footnotes
    Support   National Institutes of Health grants R01 EY019112, R01 EY018853 and R01 EB004640; the Department of Veterans Affairs, Arnold and Mabel Beckman Initiative for Macular Research.
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 1636. doi:
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    • Get Citation

      Ipek Oguz, Li Zhang, Andreas Wahle, Milan Sonka, Michael David Abramoff; Graph-based fluid segmentation from OCT images. Invest. Ophthalmol. Vis. Sci. 2016;57(12):1636.

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      © 2017 Association for Research in Vision and Ophthalmology.

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Abstract

Purpose : Accurate and reproducible segmentation of fluid-filled regions from retinal OCT images is an important step allowing quantification of the disease status, longitudinal disease progression, and response to therapy in wet-pathology retinal diseases. However, segmentation of fluid-filled regions from OCT images is a challenging task due to their inhomogeneous appearance, the unpredictability of their number, size, location, and image appearance.

Methods : We developed a knowledge-based approach that leverages a carefully designed cost function and graph-based segmentation techniques to provide a vendor-independent solution to this problem. The cost function reflects the known properties of the SEAD’s (Symptomatic Exudate Associated Derangements) in a layer-specific manner. Retinal tissue characteristics are determined directly from the image, rather than enforcing a priori intensity models. We evaluate this approach on two publicly available datasets. The OPTIMA Cyst Segmentation Challenge training data consists of 15 AMD patients acquired with devices from 4 different vendors (Spectralis, Cirrus, Topcon, Nidek), with 49 to 128 B-scans per OCT volume. The DME dataset (http://people.duke.edu/~sf59/Chiu_BOE_2014_dataset.htm) consists of 10 Spectralis scans with 61 B-scans each.

Results : Compared to a previous machine-learning based approach, the absolute volume similarity error in the DME dataset was dramatically reduced from 81.3 ± 56.4% to 43.0 ± 37.4% (paired t-test, p << 0.01). Comparison to the first (second) manual rater resulted in 87% vs. 46.4% (75.2% vs. 39.5%) volume dissimilarity for the previous and new methods. Figs. 1 and 2 present qualitative results.

Conclusions : The preliminary results are highly promising for robust vendor-independent segmentation of fluid-filled regions from OCT scans in exudative retinal diseases.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

 

Segmentation results in the DME dataset.

Segmentation results in the DME dataset.

 

Segmentation results in the OPTIMA dataset.

Segmentation results in the OPTIMA dataset.

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