Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology

被引：112

作者：

Srinivasan, Pratul P. ^{[1
,5
]}

Heflin, Stephanie J. ^{[2
]}

Izatt, Joseph A. ^{[1
,2
]}

Arshavsky, Vadim Y. ^{[2
,3
]}

Farsiu, Sina ^{[1
,2
,4
,5
]}

机构：

[1] Duke Univ, Dept Biomed Engn, Durham, NC 27708 USA

[2] Duke Univ, Med Ctr, Dept Ophthalmol, Durham, NC 27710 USA

[3] Duke Univ, Med Ctr, Dept Pharmacol & Canc Biol, Durham, NC 27710 USA

[4] Duke Univ, Dept Elect & Comp Engn, Durham, NC 27708 USA

[5] Duke Univ, Dept Comp Sci, Durham, NC 27708 USA

来源：

BIOMEDICAL OPTICS EXPRESS | 2014年 / 5卷 / 02期

关键词：

OPTICAL COHERENCE TOMOGRAPHY; MACHINE LEARNING CLASSIFIERS; INDIRECT OPHTHALMOSCOPY VIO; VESSEL SEGMENTATION; RODENT RETINA; CLASSIFICATION; DEGENERATION; PARAMETERS; THICKNESS; EYES;

D O I：

10.1364/BOE.5.000348

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Accurate quantification of retinal layer thicknesses in mice as seen on optical coherence tomography (OCT) is crucial for the study of numerous ocular and neurological diseases. However, manual segmentation is time-consuming and subjective. Previous attempts to automate this process were limited to high-quality scans from mice with no missing layers or visible pathology. This paper presents an automatic approach for segmenting retinal layers in spectral domain OCT images using sparsity based denoising, support vector machines, graph theory, and dynamic programming (S-GTDP). Results show that this method accurately segments all present retinal layer boundaries, which can range from seven to ten, in wild-type and rhodopsin knockout mice as compared to manual segmentation and has a more accurate performance as compared to the commercial automated Diver segmentation software. (C) 2014 Optical Society of America

引用

页码：348 / 365

页数：18

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