Shared-hole graph search with adaptive constraints for 3D optic nerve head optical coherence tomography image segmentation

Kai Yu; Fei Shi; Enting Gao; Weifang Zhu; Haoyu Chen; Xinjian Chen

doi:10.1364/BOE.9.000962

Shared-hole graph search with adaptive constraints for 3D optic nerve head optical coherence tomography image segmentation

Biomed Opt Express. 2018 Feb 2;9(3):962-983. doi: 10.1364/BOE.9.000962. eCollection 2018 Mar 1.

Authors

Kai Yu^{1

2}, Fei Shi^{1

2}, Enting Gao¹, Weifang Zhu¹, Haoyu Chen³, Xinjian Chen^{1

4}

Affiliations

¹ School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China.
² Indicates these authors contributed equally.
³ Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou 515041, China.
⁴ corresponding author: xjchen@suda.edu.cn.

Abstract

Optic nerve head (ONH) is a crucial region for glaucoma detection and tracking based on spectral domain optical coherence tomography (SD-OCT) images. In this region, the existence of a "hole" structure makes retinal layer segmentation and analysis very challenging. To improve retinal layer segmentation, we propose a 3D method for ONH centered SD-OCT image segmentation, which is based on a modified graph search algorithm with a shared-hole and locally adaptive constraints. With the proposed method, both the optic disc boundary and nine retinal surfaces can be accurately segmented in SD-OCT images. An overall mean unsigned border positioning error of 7.27 ± 5.40 µm was achieved for layer segmentation, and a mean Dice coefficient of 0.925 ± 0.03 was achieved for optic disc region detection.

Keywords: (100.0100) Image processing; (100.2960) Image analysis; (170.4470) Ophthalmology; (170.4500) Optical coherence tomography.