April 2014
Volume 55, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2014
Automated 3-D Retinal Layer Segmentation of Macular OCT Images with Retinal Pigment Epithelial Detachments
Author Affiliations & Notes
  • Fei Shi
    Soochow Univ., Suzhou, China
  • Xinjian Chen
    Soochow Univ., Suzhou, China
  • Weifang Zhu
    Soochow Univ., Suzhou, China
  • Dehui Xiang
    Soochow Univ., Suzhou, China
  • Enting Gao
    Soochow Univ., Suzhou, China
  • Haoyu Chen
    Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou, China
  • Footnotes
    Commercial Relationships Fei Shi, None; Xinjian Chen, None; Weifang Zhu, None; Dehui Xiang, None; Enting Gao, None; Haoyu Chen, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 4798. doi:
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      Fei Shi, Xinjian Chen, Weifang Zhu, Dehui Xiang, Enting Gao, Haoyu Chen; Automated 3-D Retinal Layer Segmentation of Macular OCT Images with Retinal Pigment Epithelial Detachments. Invest. Ophthalmol. Vis. Sci. 2014;55(13):4798.

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

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Abstract
 
Purpose
 

Automated retinal layer segmentation of OCT images has been successful for normal eyes but remains an open problem for eyes with retinal diseases. We present a method to automatically segment the retinal layers in 3-D OCT data with retinal pigment epithelial detachments (PEDs), which is a prominent feature of many chorioretinal disease processes.

 
Methods
 

Macular-centered SD-OCT (Topcon, 512×64×480 voxels, 11.72×93.75×3.50µm3) of 9 eyes diagnosed with PEDs were acquired. Automated 3-D retinal layer segmentation based on graph search was achieved as follows: First, surfaces 1-6, which were not dramatically affected by PED, and a surface combined by surface 7 and 10 (surface 7’) were detected by a multi-resolution approach[1]. Then the floor of RPE layer (surface 11) was detected below surface 7’ with large smoothness constraints to allow the abrupt change caused by PED. A surface (surface 12) indicating the normal RPE floor was detected using the same cost function but small smoothness constraints. Subsequently, the PED footprints indicating which A-scans were associated with PED were obtained by calculating the distance between surface 11 and 12. Finally, the OCT volume was flattened using a reference surface combined by surface 11 and 12, which removed the PED area and regained the normal appearance of surfaces 7 and below. In this flattened image, surface 7-10 were detected with corrections around the PED area.

 
Results
 

Manual segmentation was performed by two experts for surfaces 1,2,4-7,10 and 11, which were discernible to human eyes. For each OCT volume 10 B-scans (every sixth among the total 64) were selected for manual tracing, and the average z-position of the two results were used as the reference standards. The overall mean unsigned border error of the automatic segmentation was 2.25±0.69 voxels (7.89±2.41µm) and was comparable to the inter-observer variability 2.15±0.53 voxels (7.54±1.87µm).

 
Conclusions
 

Automated 3-D retinal layer segmentation of macular OCT images with PED has been achieved. Further analysis on retinal layer thickness, intensity and PED area/volume can be applied based on the segmentation result, which may help the diagnosis of various diseases related to PED.

 
 
Figure 1: Segmentation result. (a) A (truncated) B-scan of macular OCT volume with PED (b) Image (a) overlaid with twelve retinal surfaces obtained by automated segmentation.
 
Figure 1: Segmentation result. (a) A (truncated) B-scan of macular OCT volume with PED (b) Image (a) overlaid with twelve retinal surfaces obtained by automated segmentation.
 
Keywords: 550 imaging/image analysis: clinical • 701 retinal pigment epithelium  
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