September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
Automated Segmentation of Retinal Layers for Identification of Retinal Pigment Epithelial Detachments in Optical Coherence Tomography Images Using Truncated Convex Priors.
Author Affiliations & Notes
  • Abhay Shah
    Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, United States
  • Zhihong Hu
    Doheny Image Analysis Laboratory, Doheny Eye Institute, Los Angeles, California, United States
  • Srinivas R Sadda
    Ophthalmology, Doheny Eye Institute - UCLA, Los Angeles, California, United States
  • Xiaodong Wu
    Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, United States
    Radiation and Oncology, University of Iowa, Iowa City, Iowa, United States
  • Footnotes
    Commercial Relationships   Abhay Shah, None; Zhihong Hu, None; Srinivas Sadda, Allergan (C), Allergan (F), Avalanche (C), Bayer (C), Carl Zeiss Meditec (F), Genetech (F), Genetech (C), Iconic (C), Novartis (C), Optos (C), Optos (F), Regeneron (C), Stem Cells Inc (C), Thrombogenics (C); Xiaodong Wu, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 5951. doi:
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      Abhay Shah, Zhihong Hu, Srinivas R Sadda, Xiaodong Wu; Automated Segmentation of Retinal Layers for Identification of Retinal Pigment Epithelial Detachments in Optical Coherence Tomography Images Using Truncated Convex Priors.. Invest. Ophthalmol. Vis. Sci. 2016;57(12):5951.

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

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Abstract

Purpose : Retinal pigment epithelial detachment (PED) is a frequent and clinically important abnormality in eyes. Automated segmentation of Outer Retinal Pigment Epithelium (ORPE) and Inner Choroid (IC) are of relevance for the identification and quantification of PED (Fig.1a). Our purpose is to describe a fully automated method to simultaneously segment three PED-related surfaces: Internal Limiting Membrane (ILM), ORPE and IC, from spectral-domain optical coherence tomography (SD-OCT) volume cubes.

Methods : The study inlcuded 15 SD-OCT (Heidelberg Spectralis) volume cubes from 15 eyes of 15 subjects (one eye per patient) with the presence of submacular vascularized/fibrovascular PED. Each OCT volume scan consisted of a macular cube of 1024 x 37 x 496 voxels with an average physical size of 5.76 mm x 4.44 mm x 1.92 mm. We segmented the OCT volumes using a novel graph search with truncated convex priors (Shah et al., MICCAI 2015). The method iteratively searches for the best segmentation solution in a sub volume of the image while enforcing truncated convex priors for surface smoothness and surface separation constraints. Segmentation results from our new method were compared with results obtained from the graph search method (Li et al., PAMI, 2006).

Results : Segmentation of the various layers are shown in Figure 1b and 1c. The severity of segmentation errors was determined by averaging the vertical difference between the expert manual tracings and automated segmentations for all volumes. The quantitative comparison of the segmentation performance is summarized in Figure 2. The proposed method significantly lowered the error for ILM (p < 0.002), ORPE (p < 0.002) and IC (p < 0.002) compared to the graph search method. The proposed method with an average computation time of 746 seconds is much faster than the graph search method with an average computation time of 6082 seconds.

Conclusions : The proposed method improves the segmentation accuracy and efficiency for the retinal surfaces ORPE and IC, which are crucial for automated identification and quantification of PEDs.

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

 

Figure 1- Segmentation results on a single B-scan from an OCT volume cube of a vascularized PED with irregular surface contour. Yellow – ILM, Red – ORPE, Blue – IC

Figure 1- Segmentation results on a single B-scan from an OCT volume cube of a vascularized PED with irregular surface contour. Yellow – ILM, Red – ORPE, Blue – IC

 

Figure 2- Quantitative results for segmentation accuracy. Errors expressed as (mean ± standard deviation).

Figure 2- Quantitative results for segmentation accuracy. Errors expressed as (mean ± standard deviation).

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