Automated 3D segmentation of intraretinal layers from optic nerve head optical coherence tomography images

Antony, Bhavna J.; Abràmoff, Michael D.; Lee, Kyungmoo; Sonkova, Pavlina; Gupta, Priya; Kwon, Young H.; Niemeijer, Meindert; Hu, Zhihong; Garvin, Mona K.

doi:10.1117/12.843928

Cited by 28 publications

(56 citation statements)

References 11 publications

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“…21) [129]. A similar approach for segmenting the intraretinal layers in ONH-centered SD-OCT volumes was reported with an accuracy similar to that of the inter-observer variability of two human experts [130]. Based on their graph-theoretic approach, a preliminary layer thickness atlas was built from a small set of normal subjects [131] and unique layer changes were demonstrated in diabetes subjects [132], [133].…”

Section: Oct Image Analysismentioning

confidence: 99%

Retinal Imaging and Image Analysis

Abràmoff

Garvin

Sonka

2010

IEEE Rev. Biomed. Eng.

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1,123

714

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Many important eye diseases as well as systemic diseases manifest themselves in the retina. While a number of other anatomical structures contribute to the process of vision, this review focuses on retinal imaging and image analysis. Following a brief overview of the most prevalent causes of blindness in the industrialized world that includes age-related macular degeneration, diabetic retinopathy, and glaucoma, the review is devoted to retinal imaging and image analysis methods and their clinical implications. Methods for 2-D fundus imaging and techniques for 3-D optical coherence tomography (OCT) imaging are reviewed. Special attention is given to quantitative techniques for analysis of fundus photographs with a focus on clinically relevant assessment of retinal vasculature, identification of retinal lesions, assessment of optic nerve head (ONH) shape, building retinal atlases, and to automated methods for population screening for retinal diseases. A separate section is devoted to 3-D analysis of OCT images, describing methods for segmentation and analysis of retinal layers, retinal vasculature, and 2-D/3-D detection of symptomatic exudate-associated derangements, as well as to OCT-based analysis of ONH morphology and shape. Throughout the paper, aspects of image acquisition, image analysis, and clinical relevance are treated together considering their mutually interlinked relationships.

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Section: Oct Image Analysismentioning

confidence: 99%

Retinal Imaging and Image Analysis

Abràmoff

Garvin

Sonka

2010

IEEE Rev. Biomed. Eng.

Self Cite

1,123

714

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“…The border positioning errors show improvement over the algorithms reported in (Lee et al, 2010; Antony et al, 2010). For example the algorithm’s overall unsigned border positioning error was 6.32 ± 2.34 μm, while the unsigned error in (Lee et al, 2010; Antony et al, 2010) were 8.98 ± 3.58 μm and 8.94 ± 3.76 μm, respectively.…”

Section: Experiments and Resultsmentioning

confidence: 72%

“…To evaluate the capability of this method, 23 datasets from two patient groups were analyzed. The first group consisted of 10 OCT images from 10 patients diagnosed with glaucoma (Antony et al, 2010). The independent standard resulted from averaging tracings from two expert observers and performance assessment results are given in Section 3.1.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map

Kafieh

Rabbani

Abràmoff

et al. 2013

Medical Image Analysis

149

107

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Optical coherence tomography (OCT) is a powerful and noninvasive method for retinal imaging. In this paper, we introduce a fast segmentation method based on a new variant of spectral graph theory named diffusion maps. The research is performed on spectral domain (SD) OCT images depicting macular and optic nerve head appearance. The presented approach does not require edge-based image information in localizing most of boundaries and relies on regional image texture. Consequently, the proposed method demonstrates robustness in situations of low image contrast or poor layer-to-layer image gradients. Diffusion mapping applied to 2D and 3D OCT datasets is composed of two steps, one for partitioning the data into important and less important sections, and another one for localization of internal layers. In the first step, the pixels/voxels are grouped in rectangular/cubic sets to form a graph node. The weights of the graph are calculated based on geometric distances between pixels/voxels and differences of their mean intensity. The first diffusion map clusters the data into three parts, the second of which is the area of interest. The other two sections are eliminated from the remaining calculations. In the second step, the remaining area is subjected to another diffusion map assessment and the internal layers are localized based on their textural similarities. The proposed method was tested on 23 datasets from two patient groups (glaucoma and normals). The mean unsigned border positioning errors (mean ± SD) was 8.52 ± 3.13 and 7.56 ± 2.95 μm for the 2D and 3D methods, respectively.

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“…So it is important to develop an efficient automatic segmentation algorithm for canine retinal OCT. Many automated segmentation algorithms have been developed for human retinal layers [3][4][5][6][7], however, currently there are limited algorithms capable of…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of canine retinal OCT using adaptive gradient enhancement and region growing

Sun

Chen

et al. 2016

SPIE Proceedings

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In recent years, several studies have shown that the canine retina model offers important insight for our understanding of human retinal diseases. Several therapies developed to treat blindness in such models have already moved onto human clinical trials, with more currently under development [1]. Optical coherence tomography (OCT) offers a high resolution imaging modality for performing in-vivo analysis of the retinal layers. However, existing algorithms for automatically segmenting and analyzing such data have been mostly focused on the human retina. As a result, canine retinal images are often still being analyzed using manual segmentations, which is a slow and laborious task. In this work, we propose a method for automatically segmenting 5 boundaries in canine retinal OCT. The algorithm employs the position relationships between different boundaries to adaptively enhance the gradient map. A region growing algorithm is then used on the enhanced gradient maps to find the five boundaries separately. The automatic segmentation was compared against manual segmentations showing an average absolute error of 5.82 ± 4.02 microns.

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Automated 3D segmentation of intraretinal layers from optic nerve head optical coherence tomography images

Cited by 28 publications

References 11 publications

Retinal Imaging and Image Analysis

Retinal Imaging and Image Analysis

Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map

Automatic segmentation of canine retinal OCT using adaptive gradient enhancement and region growing

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