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

被引：17

作者：

Yu, Kai ^{[1
]}

Shi, Fei ^{[1
]}

Gao, Enting ^{[1
]}

Zhu, Weifang ^{[1
]}

Chen, Haoyu ^{[2
,3
]}

Chen, Xinjian ^{[1
]}

机构：

[1] Soochow Univ, Sch Elect & Informat Engn, Suzhou 215006, Peoples R China

[2] Shantou Univ, Joint Shantou Int Eye Ctr, Shantou 515041, Peoples R China

[3] Chinese Univ Hong Kong, Shantou 515041, Peoples R China

来源：

BIOMEDICAL OPTICS EXPRESS | 2018年 / 9卷 / 03期

基金：

中国国家自然科学基金;

关键词：

RETINAL LAYER SEGMENTATION; AUTOMATIC SEGMENTATION; OCT IMAGES; GLAUCOMA; DISC; FEATURES; PEOPLE; NUMBER;

D O I：

10.1364/BOE.9.000962

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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 mu m was achieved for layer segmentation, and a mean Dice coefficient of 0.925 +/- 0.03 was achieved for optic disc region detection. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

引用

页码：962 / 983

页数：22

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