Automated choroidal segmentation of 1060 nm OCT in healthy and pathologic eyes using a statistical model

Kajić, Vedran; Esmaeelpour, Marieh; Považay, Boris; Marshall, David; Rosin, Paul L.; Drexler, Wolfgang

doi:10.1364/boe.3.000086

Cited by 119 publications

(75 citation statements)

References 24 publications

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“…We and others have previously developed fully automated 3D algorithms for segmenting choroid on standard clinically available SD-OCT scans. [9][10][11][12][13][14] In our approach, choroidal thickness was estimated by defining the Euclidian distance between the enveloping surfaces of a choroidal vasculature segmentation (see Fig. 3).…”

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confidence: 99%

Validity of Automated Choroidal Segmentation in SS-OCT and SD-OCT

Zhang

Buitendijk

Lee

et al. 2015

Invest. Ophthalmol. Vis. Sci.

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PURPOSE.To evaluate the validity of a novel fully automated three-dimensional (3D) method capable of segmenting the choroid from two different optical coherence tomography scanners: swept-source OCT (SS-OCT) and spectral-domain OCT (SD-OCT). METHODS.One hundred eight subjects were imaged using SS-OCT and SD-OCT. A 3D method was used to segment the choroid and quantify the choroidal thickness along each A-scan. The segmented choroidal posterior boundary was evaluated by comparing to manual segmentation. Differences were assessed to test the agreement between segmentation results of the same subject. Choroidal thickness was defined as the Euclidian distance between Bruch's membrane and the choroidal posterior boundary, and reproducibility was analyzed using automatically and manually determined choroidal thicknesses.RESULTS. For SS-OCT, the average choroidal thickness of the entire 6-by 6-mm 2 macular region was 219.5 lm (95% confidence interval [CI], 204.9-234.2 lm), and for SD-OCT it was 209.5 lm (95% CI, 197.9-221.0 lm). The agreement between automated and manual segmentations was high: Average relative difference was less than 5 lm, and average absolute difference was less than 15 lm. Reproducibility of choroidal thickness between repeated SS-OCT scans was high (coefficient of variation [CV] of 3.3%, intraclass correlation coefficient [ICC] of 0.98), and differences between SS-OCT and SD-OCT results were small (CV of 11.0%, ICC of 0.73). CONCLUSIONS.We have developed a fully automated 3D method for segmenting the choroid and quantifying choroidal thickness along each A-scan. The method yielded high validity. Our method can be used reliably to study local choroidal changes and may improve the diagnosis and management of patients with ocular diseases in which the choroid is affected.

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confidence: 99%

Validity of Automated Choroidal Segmentation in SS-OCT and SD-OCT

Zhang

Buitendijk

Lee

et al. 2015

Invest. Ophthalmol. Vis. Sci.

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“…A detailed quantitative comparison with the results from Ref. 43 is not possible due to the different error measure used there. Since their method involves model parameters generated from the examination of a training set, it is unclear whether the measurements are externally repeatable.…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

“…28,36,37 Therefore, an automated detection of the OCB is highly desirable but has been realized only in a small number of studies until now, applying successful approaches for retinal layer segmentation to OCB detection. The studies 36,[38][39][40][41][42] pursue graph-theoretical approaches while 43 adapted their statistical segmentation technique from their previous work. 22 In the present study, we establish a novel method for the automated detection of the OCB within SD-OCT image data.…”

Section: Introductionmentioning

confidence: 99%

Automated detection of the choroid boundary within OCT image data using quadratic measure filters

et al. 2017

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Abstract. A novel method for the automated detection of the outer choroid boundary within spectral-domain optical coherence tomography image data, based on an image model within the space of functions of bounded variation and the application of quadratic measure filters, is presented. The same method is used for the segmentation of retinal layer boundaries and proves to be suitable even for data generated without special imaging modes and moderate line averaging. Based on the segmentations, an automated determination of the central fovea region and choroidal thickness measurements for this and two adjacent 1-mm regions are provided. The quality of the method is assessed by comparison with manual delineations performed by five trained graders. The study is based on data from 50 children of the ages 8 to 13 that were obtained in the framework of the LIFE Child study at Leipzig University.

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“…Several approaches have been proposed to segment OCT layers using both 2D and 3D techniques [26,27,28,29]. The method in [26] segments each A-scan line based on the coherence structure information extracted from the OCT signal intensity after enhancing with diffusion filtering.…”

Section: Layer Segmentationmentioning

confidence: 99%

Adaptive-weighted bilateral filtering and other pre-processing techniques for optical coherence tomography

Anantrasirichai

Nicholson

Morgan

et al. 2014

Computerized Medical Imaging and Graphics

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This paper presents novel pre-processing image enhancement algorithms for retinal optical coherence tomography (OCT). These images contain a large amount of speckle causing them to be grainy and of very low contrast. To make these images valuable for clinical interpretation, we propose a novel method to remove speckle, while preserving useful information contained in each retinal layer. The process starts with multi-scale despeckling based on a dual-tree complex wavelet transform (DT-CWT). We further enhance the OCT image through a smoothing process that uses a novel adaptiveweighted bilateral filter (AWBF). This offers the desirable property of preserving texture within the OCT image layers. The enhanced OCT image is then segmented to extract inner retinal layers that contain useful information for eye research. Our layer segmentation technique is also performed in the DT-CWT domain. Finally we describe an OCT/fundus image registration algorithm which is helpful when two modalities are used together for diagnosis and for information fusion.

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Automated choroidal segmentation of 1060 nm OCT in healthy and pathologic eyes using a statistical model

Cited by 119 publications

References 24 publications

Validity of Automated Choroidal Segmentation in SS-OCT and SD-OCT

Validity of Automated Choroidal Segmentation in SS-OCT and SD-OCT

Automated detection of the choroid boundary within OCT image data using quadratic measure filters

Adaptive-weighted bilateral filtering and other pre-processing techniques for optical coherence tomography

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