June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
Automated Segmentation of Retinal Layers for Optic Nerve Head Centered OCT Images
Author Affiliations & Notes
  • Xinjian Chen
    School of Electronics and Information Engineering, Soochow University, Suzhou, China
  • Enting Gao
    School of Electronics and Information Engineering, Soochow University, Suzhou, China
  • Fei Shi
    School of Electronics and Information Engineering, Soochow University, Suzhou, China
  • Weifang Zhu
    School of Electronics and Information Engineering, Soochow University, Suzhou, China
  • Haoyu Chen
    Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong , Shantou, China
  • Footnotes
    Commercial Relationships   Xinjian Chen, None; Enting Gao, None; Fei Shi, None; Weifang Zhu, None; Haoyu Chen, None
  • Footnotes
    Support  This work was supported by the National Basic Research Program of China (973 Program) under Grant 2014CB748600, in part by the National Natural Science Foundation of China (NSFC) under Grant 81371629.
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 669. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to Subscribers Only
      Sign In or Create an Account ×
    • Get Citation

      Xinjian Chen, Enting Gao, Fei Shi, Weifang Zhu, Haoyu Chen; Automated Segmentation of Retinal Layers for Optic Nerve Head Centered OCT Images. Invest. Ophthalmol. Vis. Sci. 2017;58(8):669.

      Download citation file:


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

      ×
  • Supplements
Abstract

Purpose : Optical coherence tomography(OCT), being a noninvasive imaging modality, has begun to find vast use in the diagnosis and management of ocular diseases. However, due to speckle noise and irregularly shaped morphological features such as optic never head(ONH), accurate segmentation of individual retinal layers is difficult.

Methods : 11 normal subjects (6 used as training set, 5 used as testing set) were included and underwent Optic Nerve Head centered SD-OCT (Topcon 3D-OCT 2000, 512×128×885 voxels, 6mm×6mm×2.3mm). The proposed framework consists of two main steps: preprocessing and layer segmentation. In the preprocessing step, a curvature anisotropic diffusion filter was used to reduce the OCT speckle noise. Then the B-scans were flattened based on surface 5 (Fig.1). Subsequently, during layer segmentation, First, the Active Appearance Models(AAM)[3] was built, and the surface 1, 2, 3, 4, 5, 6, 7 and the optic disc was detected. Second, a multi-resolution graph-search algorithm[1], is applied for the further precise segmentation. Finally, the rim region of the optic disc was detected by shape prior model on the projection image from surface 6 to surface 7. The rim region is masked out because the layers are hard to define in this region.

Results : The algorithm was tested against the ground truth which manually traced by two retinal specialists independently. The results are accordance with the ground truth well(Fig.2).

Conclusions : In this paper, we proposed an automated method for retinal layers segmentation in ONH centered 3-D OCT images, which addresses the challenges posed by the presence of the large blood vessels and the optic disc. The preliminary results show the feasibility and efficiency of the proposed method.

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

 

Fig.1 ONH centered OCT image of a normal eye and the 7 surfaces of the retinal layers. (a)original image (b) denoised and flattened image with segmentation result.

Fig.1 ONH centered OCT image of a normal eye and the 7 surfaces of the retinal layers. (a)original image (b) denoised and flattened image with segmentation result.

 

Fig.2 A central B-scan of a dataset with yellow indicating the rim region.

Fig.2 A central B-scan of a dataset with yellow indicating the rim region.

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×