Drusen Volume and Retinal Pigment Epithelium Abnormal Thinning Volume Predict 2-Year Progression of Age-Related Macular Degeneration

Folgar, Francisco A.; Yuan, Eric; Sevilla, Monica; Chiu, Stephanie J.; Farsiu, Sina; Chew, Emily Y.; Toth, Cynthia A.

doi:10.1016/j.ophtha.2015.09.016

Cited by 94 publications

(106 citation statements)

References 36 publications

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“…However, this work did not produce outlines describing the GA region, as this was not the main focus of this work. The study of volume measurement of RPE as predictors of progression to advanced AMD has been reported by Folgar et al [16]. They discussed that the RPE-drusen complex abnormal thinning is believed to be an early precursor to the formation of GA lesions as well as a biomarker for the presence of any noncentral GA and the development of central GA over 2-year follow-up.…”

Section: Introductionmentioning

confidence: 95%

Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor

Niu¹,

Sisternes²,

Chen³

et al. 2016

Biomed. Opt. Express

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Age-related macular degeneration (AMD) is the leading cause of blindness among elderly individuals. Geographic atrophy (GA) is a phenotypic manifestation of the advanced stages of non-exudative AMD. Determination of GA extent in SD-OCT scans allows the quantification of GA-related features, such as radius or area, which could be of important value to monitor AMD progression and possibly identify regions of future GA involvement. The purpose of this work is to develop an automated algorithm to segment GA regions in SD-OCT images. An en face GA fundus image is generated by averaging the axial intensity within an automatically detected sub-volume of the three dimensional SD-OCT data, where an initial coarse GA region is estimated by an iterative threshold segmentation method and an intensity profile set, and subsequently refined by a region-based Chan-Vese model with a local similarity factor. Two image data sets, consisting on 55 SD-OCT scans from twelve eyes in eight patients with GA and 56 SD-OCT scans from 56 eyes in 56 patients with GA, respectively, were utilized to quantitatively evaluate the automated segmentation algorithm. We compared results obtained by the proposed algorithm, manual segmentation by graders, a previously proposed method, and experimental commercial software. When compared to a manually determined gold standard, our algorithm presented a mean overlap ratio (OR) of 81.86% and 70% for the first and second data sets, respectively, while the previously proposed method OR was 72.60% and 65.88% for the first and second data sets, respectively, and the experimental commercial software OR was 62.40% for the second data set.

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Section: Introductionmentioning

confidence: 95%

Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor

Niu¹,

Sisternes²,

Chen³

et al. 2016

Biomed. Opt. Express

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“…In our study population, 11% of individual drusen regressed within the 12 months. Only drusen larger than 0.001 mm 3 and within 5 mm circle around fovea are considered, as done similarly in [2,10]. The prediction performance was evaluated using leave-one-patient-out cross-validation.…”

Section: Resultsmentioning

confidence: 99%

“…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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“…Alternatively, we defined the limits of drusen areas to coincide with those of the RPEDC, namely IRPEDC and BM. The RPEDC is composed by RPE layer, drusen material and other structures related to AMD, such as subretinal drusen, hyperreflective foci and drusen remnants over GA [6]. Folgar et al [6] presented strong evidence suggesting that the quantification of this layer in drusen regions can be used as a biomarker for the progression of AMD.…”

Section: Analysis Of Drusen Areas Measurementsmentioning

confidence: 99%

“…These lesions can have quite different shape, size, composition, color and border definition [1]. Subretinal drusen are also indicative of progression of AMD and composed of the same material as regular drusen, the main difference is the accumulation of extracellular material in the inner side of RPE [6]. In this paper, we will consider the limits of drusen as the IRPEDC and BM boundaries ( Fig.…”

Section: Introductionmentioning

confidence: 99%

Multi-surface segmentation of OCT images with AMD using sparse high order potentials

Oliveira

Pereira

Gonçalves³

et al. 2016

Biomed. Opt. Express

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In age-related macular degeneration (AMD), the quantification of drusen is important because it is correlated with the evolution of the disease to an advanced stage. Therefore, we propose an algorithm based on a multi-surface framework for the segmentation of the limiting boundaries of drusen: the inner boundary of the retinal pigment epithelium + drusen complex (IRPEDC) and the Bruch's membrane (BM). Several segmentation methods have been considerably successful in segmenting retinal layers of healthy retinas in optical coherence tomography (OCT) images. These methods are successful because they incorporate prior information and regularization. Nonetheless, these factors tend to hinder the segmentation for diseased retinas. The proposed algorithm takes into account the presence of drusen and geographic atrophy (GA) related to AMD by excluding prior information and regularization just valid for healthy regions. However, even with this algorithm, prior information and regularization still cause the oversmoothing of drusen in some locations. Thus, we propose the integration of local shape prior in the form of a sparse high order potentials (SHOPs) into the algorithm to reduce the oversmoothing of drusen. The proposed algorithm was evaluated in a public database. The mean unsigned errors, relative to the average of two experts, for the inner limiting membrane (ILM), IRPEDC and BM were 2.94±2.69, 5.53±5.66 and 4.00±4.00 µm, respectively. Drusen areas measurements were evaluated, relative to the average of two expert graders, by the mean absolute area difference and overlap ratio, which were 1579.7 ± 2106.8 µm 2 and 0.78 ± 0.11, respectively.

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Drusen Volume and Retinal Pigment Epithelium Abnormal Thinning Volume Predict 2-Year Progression of Age-Related Macular Degeneration

Cited by 94 publications

References 36 publications

Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor

Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor

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

Multi-surface segmentation of OCT images with AMD using sparse high order potentials

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