Quantitative SD-OCT Imaging Biomarkers as Indicators of Age-Related Macular Degeneration Progression

Sisternes, Luís de; Simon, Noah; Tibshirani, Robert; Leng, Theodore; Rubin, Daniel L.

doi:10.1167/iovs.14-14918

Cited by 125 publications

(117 citation statements)

References 41 publications

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“…In Tijuana, TB control is overseen by the Secretary of Health of Baja California at the state level and is managed by the local sanitary jurisdiction in conjunction with care delivery through local health centers. 21 Recruitment and enrollment A convenience sample of TB patients, care providers (e.g., physicians, nurses and DOT workers) and local TB public health officials were invited to participate in focus groups. TB patients who had completed treatment within the past six months or were still on treatment but non-infectious, and had received …”

Section: Study Populationmentioning

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“…20 However, like any other technologies, the adoption is complex because it does not occur in a vacuum, and it involves interaction with the user over time. 21 Technologies are constantly advancing; therefore, implementation of new programs should be flexible and contextual. 22 How users Á practitioners and patients -perceive of and interact with the technologies is crucial to implementing a successful digital health program.…”

Section: Research Questionsmentioning

confidence: 99%

“…20 Additionally, a team has automated the quantification of the number, morphology, and reflective properties of drusen based on spectral domain-optical coherence tomography. 21 An alternative solution is crowdsourcing. In two studies, researchers demonstrated the utility of crowdsourcing of untrained people looking at retina images in automated diagnosis.…”

Section: Image Recognition and Interpretationmentioning

confidence: 99%

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2016

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“…The ability of SD-OCT to rapidly acquire three-dimensional (3D) retinal data, resolving structures in the depth axis, allows the direct visualization of its layered structure. Many new quantitative features extracted from SD-OCT data, such as the thickness of individual intraretinal layers [2], size, shape, and distribution of drusen (extracellular material accumulations that typically appear between the retinal pigment epithelium (RPE) and Bruch's membrane) [3,4], cysts and fluid-filled regions [5], and macular [6], are currently used or investigated as biomarkers in retinal disease diagnosis [7][8][9]. The accurate and reliable segmentation of retinal layers in SD-OCT scans is a fundamental problem for the identification of new quantitative features that could be useful as disease biomarkers.…”

Section: Introductionmentioning

confidence: 99%

Automated intraretinal segmentation of SD-OCT images in normal and age-related macular degeneration eyes

Sisternes

Jonna

Moss

et al. 2017

Biomed. Opt. Express

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This work introduces and evaluates an automated intra-retinal segmentation method for spectral-domain optical coherence (SD-OCT) retinal images. While quantitative assessment of retinal features in SD-OCT data is important, manual segmentation is extremely time-consuming and subjective. We address challenges that have hindered prior automated methods, including poor performance with diseased retinas relative to healthy retinas, and data smoothing that obscures image features such as small retinal drusen. Our novel segmentation approach is based on the iterative adaptation of a weighted median process, wherein a three-dimensional weighting function is defined according to image intensity and gradient properties, and a set of smoothness constraints and pre-defined rules are considered. We compared the segmentation results for 9 segmented outlines associated with intra-retinal boundaries to those drawn by hand by two retinal specialists and to those produced by an independent state-of-the-art automated software tool in a set of 42 clinical images (from 14 patients). These images were obtained with a Zeiss Cirrus SD-OCT system, including healthy, early or intermediate AMD, and advanced AMD eyes. As a qualitative evaluation of accuracy, a highly experienced third independent reader blindly rated the quality of the outlines produced by each method. The accuracy and image detail of our method was superior in healthy and early or intermediate AMD eyes (98.15% and 97.78% of results not needing substantial editing) to the automated method we compared against. While the performance was not as good in advanced AMD (68.89%), it was still better than the manual outlines or the comparison method (which failed in such cases). We also tested our method's performance on images acquired with a different SD-OCT manufacturer, collected from a large publicly available data set (114 healthy and 255 AMD eyes), and compared the data quantitatively to reference standard markings of the internal limiting membrane and inner boundary of retinal pigment epithelium, producing a mean unsigned positioning error of 6.04 ± 7.83µm (mean under 2 pixels). Our automated method should be applicable to data from different OCT manufacturers and offers detailed layer segmentations in healthy and AMD eyes.

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“…Those methods generally work well except for possible undersegmentation of very small or very large drusen. The subsequent steps consisted of quantifying and characterizing the drusen load in longitudinal studies for the purpose of estimating the risk of AMD progression [2,9,10]. Nevertheless, there is still a lack of sensitive and specic structural biomarkers predictive of late AMD at the individual patient level.…”

Section: Introductionmentioning

confidence: 99%

Predicting Drusen Regression from OCT in Patients with Age-Related Macular Degeneration

Bogunović

Montuoro

Waldstein

et al. 2016

Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop

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Abstract. Age-related macular degeneration (AMD) is a leading cause of blindness in developed countries. The presence of drusen is the hallmark of early/intermediate AMD, and their sudden regression is strongly associated with the onset of late AMD. In this work we propose a predictive model of drusen regression using optical coherence tomography (OCT) based features. First, a series of automated image analysis steps are applied to segment and characterize individual drusen and their development. Second, from a set of quantitative features, a random forest classier is employed to predict the occurrence of individual drusen regression within the following 12 months. The predictive model is trained and evaluated on a longitudinal OCT dataset of 44 eyes from 26 patients using leave-one-patient-out cross-validation. The model achieved an area under the ROC curve of 0.81, with a sensitivity of 0.74 and a specicity of 0.73. The presence of hyperreective foci and mean drusen signal intensity were found to be the two most important features for the prediction. This preliminary study shows that predicting drusen regression is feasible and is a promising step toward identication of imaging biomarkers of incoming regression.

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Quantitative SD-OCT Imaging Biomarkers as Indicators of Age-Related Macular Degeneration Progression

Abstract: Our results demonstrate the potential ability of our model to identify those AMD patients at risk of progressing to exudative AMD from an early or intermediate stage.

Cited by 125 publications

References 41 publications

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Automated intraretinal segmentation of SD-OCT images in normal and age-related macular degeneration eyes

Predicting Drusen Regression from OCT in Patients with Age-Related Macular Degeneration

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