Individual Drusen Segmentation and Repeatability and Reproducibility of Their Automated Quantification in Optical Coherence Tomography Images

Spectral-Domain and Swept-Source OCT Angiographic Scans Yield Similar Drusen Measurements When Processed with the Same Algorithm

Purpose

An algorithm developed to obtain drusen area and volume measurements using swept-source OCT angiography (SS-OCTA) scans was tested on spectral-domain OCT angiography (SD-OCTA) scans.

Design

Retrospective study.

Participants

Forty pairs of scans from 27 eyes with intermediate age-related macular degeneration and drusen.

Methods

Patients underwent both SD-OCTA and SS-OCTA imaging at the same visit using the 6 mm × 6 mm OCTA scan patterns. Using the same algorithm, we obtained drusen area and volume measurements within both 3 mm and 5 mm fovea-centered circles. Paired 2-sample t-tests were performed along with Pearson's correlation tests.

Main Outcome Measures

Mean square root (sqrt) drusen area and cube root (cbrt) drusen volume within the 3 mm and 5 mm fovea-centered circles.

Results

Mean sqrt drusen area values from SD-OCTA and SS-OCTA scans were 1.57 (standard deviation [SD] 0.57) mm and 1.49 (SD 0.58) mm in the 3 mm circle and 1.88 (SD 0.59) mm and 1.76 (SD 0.58) mm in the 5 mm circle, respectively. Mean cbrt drusen volume measurements were 0.54 (SD 0.19) mm and 0.51 (SD 0.20) mm in the 3 mm circle, and 0.60 (SD 0.17) mm and 0.57 (SD 0.17) mm in the 5 mm circle. Small differences in area and volume measurements were found (all P < 0.001); however, the correlations between the instruments were strong (all coefficients > 0.97; all P < 0.001).

Conclusions

An algorithm originally developed for SS-OCTA scans performs well when used to obtain drusen volume and area measurements from SD-OCTA scans; thus, a separate SD-OCT structural scan is unnecessary to obtain measurements of drusen.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Retinal pigment epithelium-Bruch's membrane volume in grading of age-related macular degeneration

AIM

To assess the agreement of optical coherence tomography (OCT) algorithm-based retinal pigment epithelium -Bruch's membrane complex volume (RBV) with fundus photograph-based age-related macular degeneration (AMD) grading.

METHODS

Digital color fundus photographs (CFPs) and spectral domain OCT images were acquired from 96 elderly subjects. CFPs were graded according to Age-Related Eye Disease Study (AREDS) classification. OCT image segmentation and RBV data calculation were done with Orion™ software. Univariate and multivariate analyses were performed to find out whether AMD lesion features associated with higher RBVs.

RESULTS

RBV correlated with AMD grading (rs=0.338, P=0.001), the correlation was slightly stronger in early AMD (n=52; rs=0.432, P=0.001). RBV was higher in subjects with early AMD compared with those with no AMD lesions evident in fundus photographs (1.05±0.20 vs 0.96±0.13 mm3, P=0.023). In multivariate analysis higher RBVs were associated significantly with higher total drusen (β=0.388, P=0.027) and pigmentation areas (β=0.319, P=0.020) in fundus photographs, whereas depigmentation area (β=-0.295, P=0.015) associated with lower RBV.

CONCLUSION

RBV correlate with AMD grading status, with a stronger association in patients with moderate, non-late AMD grades. This effect is driven mostly by lesions with drusen or pigmentation. Lesions with depigmentation tend to have lower values. RBV is more comprehensive measurement of the key area of AMD pathogenesis, compared to sole drusen volume analysis. RBV measurements are independent on grader variations and offer a possibility to quantify early and middle grade AMD lesions in a research setting, but may not substitute fundus photograph-based grading in the whole range of AMD spectrum.

Six-year incidence of age-related macular degeneration and correlation to OCT-derived drusen volume measurements in a Chinese population

AIMS

To report the 6-year incidence of optical coherence tomography (OCT)-derived age-related changes in drusen volume and related systemic and ocular associations.

METHODS

Chinese adults aged 40 years and older were assessed at baseline and 6 years with colour fundus photography (CFP) and spectral domain (SD) OCT. CFPs were graded for age-related macular degeneration (AMD) features and drusen volume was generated using commercially available automated software.

RESULTS

A total of 4172 eyes of 2580 participants (mean age 58.12±9.03 years; 51.12% women) had baseline and 6-year follow-up CFP for grading, of these, 2130 eyes of 1305 participants had gradable SD-OCT images, available for analysis. Based on CFP grading, 136 (3.39%) participants developed incident early AMD and 10 (0.25%) late AMD. Concurrently, retinal pigment epithelial-Bruch's membrane (RPE-BrC) volumes decreased, remained stable and increased in 6.8%, 78.5% and 14.7%, respectively, over 6 years. In eyes where RPE-BrC volumes were >0 mm³ at baseline, this was associated with two-fold higher prevalence rate of any AMD at baseline (p<0.001). Multivariable analysis showed that when compared with eyes where RPE-BrC volume was unchanged, volume decrease was significantly associated with older age (OR=1.30; p<0.001), smoking (OR=2.21; p=0.001) and chronic kidney disease (OR=3.4, p=0.008), while increase was associated with older age (OR=1.36; p<0.001) and hypertension (OR=1.43; p=0.016).

CONCLUSION

AMD incidence detected at 6 years on CFP and correlated OCT-derived drusen volume measurement change is low. Older age and some systemic risk factors are associated with drusen volume change, and our data provide new insights into relationship between systemic risk factors and outer retinal morphology in Asian eyes.

Improving foveal avascular zone segmentation in fluorescein angiograms by leveraging manual vessel labels from public color fundus pictures

In clinical routine, ophthalmologists frequently analyze the shape and size of the foveal avascular zone (FAZ) to detect and monitor retinal diseases. In order to extract those parameters, the contours of the FAZ need to be segmented, which is normally achieved by analyzing the retinal vasculature (RV) around the macula in fluorescein angiograms (FA). Computer-aided segmentation methods based on deep learning (DL) can automate this task. However, current approaches for segmenting the FAZ are often tailored to a specific dataset or require manual initialization. Furthermore, they do not take the variability and challenges of clinical FA into account, which are often of low quality and difficult to analyze. In this paper we propose a DL-based framework to automatically segment the FAZ in challenging FA scans from clinical routine. Our approach mimics the workflow of retinal experts by using additional RV labels as a guidance during training. Hence, our model is able to produce RV segmentations simultaneously. We minimize the annotation work by using a multi-modal approach that leverages already available public datasets of color fundus pictures (CFPs) and their respective manual RV labels. Our experimental evaluation on two datasets with FA from 1) clinical routine and 2) large multicenter clinical trials shows that the addition of weak RV labels as a guidance during training improves the FAZ segmentation significantly with respect to using only manual FAZ annotations.

Quantitative assessment of retinal fluid in neovascular age-related macular degeneration under anti-VEGF therapy

The retinal world has been revolutionized by optical coherence tomography (OCT) and anti-vascular endothelial growth factor (VEGF) therapy. The numbers of intravitreal injections are on a constant rise and management in neovascular age-related macular degeneration (nAMD) is mainly driven by the qualitative assessment of macular fluid as detected on OCT scans. The presence of macular fluid, particularly subretinal fluid (SRF) and intraretinal fluid (IRF), has been used to trigger re-treatments in clinical trials and the real world. However, large discrepancies can be found between the evaluations of different readers or experts and especially small amounts of macular fluid might be missed during this process. Pixel-wise detection of macular fluid uses an entire OCT volume to calculate exact volumes of retinal fluid. While manual annotations of such pixel-wise fluid detection are unfeasible in a clinical setting, artificial intelligence (AI) is able to overcome this hurdle by providing real-time results of macular fluid in different retinal compartments. Quantitative fluid assessments have been used for various post hoc analyses of randomized controlled trials, providing novel insights into anti-VEGF treatment regimens. Nonetheless, the application of AI-algorithms in a prospective patient care setting is still limited. In this review, we discuss the use of quantitative fluid assessment in nAMD during anti-VEGF therapy and provide an outlook to novel forms of patient care with the support of AI quantifications.

Imaging and artificial intelligence for progression of age-related macular degeneration

Age-related macular degeneration (AMD) is a leading cause of severe vision loss. With our aging population, it may affect 288 million people globally by the year 2040. AMD progresses from an early and intermediate dry form to an advanced one, which manifests as choroidal neovascularization and geographic atrophy. Conversion to AMD-related exudation is known as progression to neovascular AMD, and presence of geographic atrophy is known as progression to advanced dry AMD. AMD progression predictions could enable timely monitoring, earlier detection and treatment, improving vision outcomes. Machine learning approaches, a subset of artificial intelligence applications, applied on imaging data are showing promising results in predicting progression. Extracted biomarkers, specifically from optical coherence tomography scans, are informative in predicting progression events. The purpose of this mini review is to provide an overview about current machine learning applications in artificial intelligence for predicting AMD progression, and describe the various methods, data-input types, and imaging modalities used to identify high-risk patients. With advances in computational capabilities, artificial intelligence applications are likely to transform patient care and management in AMD. External validation studies that improve generalizability to populations and devices, as well as evaluating systems in real-world clinical settings are needed to improve the clinical translations of artificial intelligence AMD applications.

Topographic Distribution and Progression of Soft Drusen Volume in Age-Related Macular Degeneration Implicate Neurobiology of Fovea

Purpose

To refine estimates of macular soft drusen abundance in eyes with age-related macular degeneration (AMD) and evaluate hypotheses about drusen biogenesis, we investigated topographic distribution and growth rates of drusen by optical coherence tomography (OCT). We compared results to retinal features with similar topographies (cone density and macular pigment) in healthy eyes.

Methods

In a prospective study, distribution and growth rates of soft drusen in eyes with AMD were identified by human observers in OCT volumes and analyzed with computer-assistance. Published histologic data for macular cone densities (n = 12 eyes) and in vivo macular pigment optical density (MPOD) measurements in older adults with unremarkable maculae (n = 31; 62 paired eyes, averaged) were revisited. All values were normalized to Early Treatment Diabetic Retinopathy Study (ETDRS) subfield areas.

Results

Sixty-two eyes of 44 patients were imaged for periods up to 78 months. Soft drusen volume per unit volume at baseline is 24.6-fold and 2.3-fold higher in the central ETDRS subfield than in outer and inner rings, respectively, and grows most prominently there. Corresponding ratios (central versus inner and central versus outer) for cone density in donor eyes is 13.3-fold and 5.1-fold and for MPOD, 24.6 and 23.9-fold, and 3.6 and 3.6-fold.

Conclusions

Normalized soft drusen volume in AMD eyes as assessed by OCT is ≥ 20-fold higher in central ETDRS subfields than in outer rings, paralleling MPOD distribution in healthy eyes. Data on drusen volume support this metric for AMD risk assessment and clinical trial outcome measure. Alignment of different data modalities support the ETDRS grid for standardizing retinal topography in mechanistic studies of drusen biogenesis.

Validation of a Novel Automated Algorithm to Measure Drusen Volume and Area Using Swept Source Optical Coherence Tomography Angiography

Purpose

The purpose of this study was to validate a novel automated swept source optical coherence tomography angiography (SS-OCTA) algorithm to measure elevations of the retinal pigment epithelium (RPE) in eyes with nonexudative age-related macular degeneration (neAMD).

Methods

Patients with drusen were enrolled in a prospective optical coherence tomography (OCT) study and underwent both spectral domain OCT (SD-OCT) and SS-OCTA imaging at the same visit using the 6 × 6 mm scan patterns. The RPE elevation measurements (square root area and cube root volume) from the SS-OCTA algorithm were compared with the automated validated SD-OCT algorithm on the instrument. Standard deviations of drusen measurements from four repeated scans of another separate set were also calculated to evaluate the reproducibility of the SS-OCTA algorithm.

Results

A total of 53 eyes from 28 patients were scanned on both instruments. A very strong correlation was found between the measurements from the two algorithms (all r > 0.95), although the measurements of the drusen area and volume were all larger from the SS-OCTA instrument. The reproducibility of the new SS-OCTA algorithm was analyzed using a sample of 66 eyes from 43 patients. The intraclass correlation coefficient (ICC) was greater than 99% from different macular regions for both the square root area and cube root volume measurements.

Conclusions

A novel automated SS-OCTA algorithm for the quantitative assessment of drusen was validated against the SD-OCT algorithm and was shown to be highly reproducible.

Translational Relevance

This novel SS-OCTA algorithm provides a strategy to measure the area and volume of drusen to assess disease progression in neAMD.

Structure-Function Analysis in Macular Drusen With Mesopic and Scotopic Microperimetry

Purpose

To investigate the structure–function relationship in eyes with drusen with mesopic and scotopic microperimetry.

Methods

We analyzed structural and functional data from 43 eyes with drusen. Functional data were acquired with mesopic and scotopic two-color (red and cyan) microperimetry. Normative values were calculated using data from 56 healthy eyes. Structural measurements were green autofluorescence and dense macular optical coherence tomography scans. The latter were used to calculate the retinal pigment epithelium elevation (RPE-E) and the photoreceptor reflectivity ratio (PRR). The pointwise structure–function relationship was measured with linear mixed models having the log-transformed structural parameters as predictors and the sensitivity loss (SL, deviation from normal) as the response variable.

Results

In the univariable analysis, the structural predictors were all significantly correlated (P < 0.05) with the SL in the mesopic and scotopic tests. In a multivariable model, mesopic microperimetry yielded the best structure–function relationship. All predictors were significant (P < 0.05), but the predictive power was weak (best R² = 0.09). The relationship was improved when analyzing locations with abnormal RPE-E (best R² = 0.18).

Conclusions

Mesopic microperimetry shows better structure–function relationship compared to scotopic microperimetry; the relationship is weak, likely due to the early functional damage and the small number of tested locations affected by drusen. The relationship is stronger when locations with drusen are isolated for the mesopic and scotopic cyan test.

Translational Relevance

These results could be useful to devise integrated structure–function methods to detect disease progression in intermediate age-related macular degeneration.

Prediction of age-related macular degeneration disease using a sequential deep learning approach on longitudinal SD-OCT imaging biomarkers

We propose a hybrid sequential prediction model called “Deep Sequence”, integrating radiomics-engineered imaging features, demographic, and visual factors, with a recursive neural network (RNN) model in the same platform to predict the risk of exudation within a future time-frame in non-exudative AMD eyes. The proposed model provides scores associated with risk of exudation in the short term (within 3 months) and long term (within 21 months), handling challenges related to variability of OCT scan characteristics and the size of the training cohort. We used a retrospective clinical trial dataset that includes 671 AMD fellow eyes with 13,954 observations before any signs of exudation for training and validation in a tenfold cross validation setting. Deep Sequence achieved high performance for the prediction of exudation within 3 months (0.96 ± 0.02 AUCROC) and within 21 months (0.97 ± 0.02 AUCROC) on cross-validation. Training the proposed model on this clinical trial dataset and testing it on an external real-world clinical dataset showed high performance for the prediction within 3-months (0.82 AUCROC) but a clear decrease in performance for the prediction within 21-months (0.68 AUCROC). While performance differences at longer time intervals may be derived from dataset differences, we believe that the high performance and generalizability achieved in short-term predictions may have a high clinical impact allowing for optimal patient follow-up, adding the possibility of more frequent, detailed screening and tailored treatments for those patients with imminent risk of exudation.

Comparison of Drusen Volume Assessed by Two Different OCT Devices

To compare drusen volume between Heidelberg Spectral Domain (SD-) and Zeiss Swept-Source (SS) PlexElite Optical Coherence Tomography (OCT) determined by manual and automated segmentation methods. Thirty-two eyes of 24 patients with Age-Related Macular Degeneration (AMD) and drusen maculopathy were included. In the central 1 and 3 mm ETDRS circle drusen volumes were calculated and compared. Drusen segmentation was performed using automated manufacturer algorithms of the two OCT devices. Then, the automated segmentation was manually corrected and compared and finally analyzed using customized software. Though on SD-OCT, there was a significant difference of mean drusen volume prior to and after manual correction (mean difference: 0.0188 ± 0.0269 mm³, p < 0.001, corr. p < 0.001, correlation of r = 0.90), there was no difference found on SS-OCT (mean difference: 0.0001 ± 0.0003 mm³, p = 0.262, corr. p = 0.524, r = 1.0). Heidelberg-acquired mean drusen volume after manual correction was significantly different from Zeiss-acquired drusen volume after manual correction (mean difference: 0.1231 ± 0.0371 mm³, p < 0.001, corr. p < 0.001, r = 0.68). Using customized software, the difference of measurements between both devices decreased and correlation among the measurements improved (mean difference: 0.0547 ± 0.0744 mm³, p = 0.02, corr. p = 0.08, r = 0.937). Heidelberg SD-OCT, the Zeiss PlexElite SS-OCT, and customized software all measured significantly different drusen volumes. Therefore, devices/algorithms may not be interchangeable. Third-party customized software helps to minimize differences, which may allow a pooling of data of different devices, e.g., in multicenter trials.

Real-time retinal layer segmentation of OCT volumes with GPU accelerated inferencing using a compressed, low-latency neural network

Segmentation of retinal layers in optical coherence tomography (OCT) is an essential step in OCT image analysis for screening, diagnosis, and assessment of retinal disease progression. Real-time segmentation together with high-speed OCT volume acquisition allows rendering of en face OCT of arbitrary retinal layers, which can be used to increase the yield rate of high-quality scans, provide real-time feedback during image-guided surgeries, and compensate aberrations in adaptive optics (AO) OCT without using wavefront sensors. We demonstrate here unprecedented real-time OCT segmentation of eight retinal layer boundaries achieved by 3 levels of optimization: 1) a modified, low complexity, neural network structure, 2) an innovative scheme of neural network compression with TensorRT, and 3) specialized GPU hardware to accelerate computation. Inferencing with the compressed network U-NetRT took 3.5 ms, improving by 21 times the speed of conventional U-Net inference without reducing the accuracy. The latency of the entire pipeline from data acquisition to inferencing was only 41 ms, enabled by parallelized batch processing. The system and method allow real-time updating of en face OCT and OCTA visualizations of arbitrary retinal layers and plexuses in continuous mode scanning. To the best our knowledge, our work is the first demonstration of an ophthalmic imager with embedded artificial intelligence (AI) providing real-time feedback.

Imaging, Genetic, and Demographic Factors Associated With Conversion to Neovascular Age-Related Macular Degeneration: Secondary Analysis of a Randomized Clinical Trial

Importance

Risk factors associated with the development of neovascular age-related macular degeneration (AMD) have been identified. However, population size and methods to integrate imaging, genetic, and demographic factors associated with conversion to neovascular AMD are limited, specifically when treatment is administered in 1 eye.

Objective

To determine the imaging, genetic, and demographic factors associated with conversion from nonneovascular to neovascular AMD in fellow eyes.

Design, Setting, and Participants

This post hoc secondary analysis of the 24-month phase 3 multicenter, double-masked, active treatment-controlled HARBOR trial included 686 fellow eyes with nonneovascular AMD at baseline. Imaging features describing the presence, number, extent, density, and relative reflectivity of drusen were automatically extracted from spectral-domain optical coherence tomography scans. Genetic analysis included 34 single-nucleotide polymorphisms. Least absolute shrinkage and selection operator regression was performed to narrow imaging features. Survival analysis and Cox proportional hazards regression were performed to determine the association of the selected imaging features and genetic and demographic factors with conversion to neovascular AMD. Data were collected from November 2016 through October 2017 and analyzed from October 2017 through October 2018.

Exposure

Nonneovascular AMD in the fellow eye.

Main Outcomes and Measures

Features associated with conversion to neovascular AMD. Hazard ratios (HRs) and their 95% CIs were calculated.

Results

Among the 686 fellow eyes included in the analysis (406 [59.2%] women; mean [SD] age, 78.12 [8.28] years), 154 (22.4%) converted to neovascular AMD. Female sex was significantly associated with conversion to neovascular AMD (HR, 1.57; 95% CI, 1.11-2.20; P = .009). After controlling for demographic and treatment effects, drusen area within 3 mm of the fovea (HR, 1.45; 95% CI, 1.24-1.69; HR for 1-SD increase, 1.36 [95% CI, 1.20-1.54]) and mean drusen reflectivity (HR, 3.97; 95% CI, 1.11-14.18; HR for 1-SD increase, 1.32 [95% CI, 1.02-1.71]) were significantly associated with conversion to neovascular AMD. In addition, 1 genetic variant (rs61941274) was found to be associated with conversion to neovascular AMD.

Conclusions and Relevance

Two imaging features (total en face area of drusen restricted to a circular area 3 mm from the fovea and mean drusen reflectivity) and 1 genetic variant (ACAD10 locus) were associated with conversion to neovascular AMD. Drusen characteristics may be associated with conversion to neovascular AMD despite treatment in 1 eye.

Trial Registration

ClinicalTrials.gov identifier: NCT00891735.

Correlation of Color Fundus Photograph Grading with Risks of Early Age-related Macular Degeneration by using Automated OCT-derived Drusen Measurements

We evaluated automated OCT-derived drusen volume measures in a population-based study (n = 4,512) aged ≥40 years, and its correlation with conventional color fundus photographs (CFP)-derived early AMD features. Participants had protocol-based assessment to capture medical and ocular history, genotyping for SNPs in CFH, ARMS2, and CETP, CFP-based AMD grading and automated drusen volume based on SD-OCT using built-in software (Cirrus OCT advanced RPE analysis software). Significantly fewer eyes with early AMD features (drusen, hyperpigmentation, soft or reticular drusen) had drusen volume = 0 mm³ (p < 0.001). In eyes with drusen volume > 0 mm³, increasing AMD severity was associated with increase in drusen volume (correlation coefficient 0.17, p < 0.001). However 220 (59.14%) of 372 participants with AMD based on CFP grading had drusen volume = 0 mm³. Factors associated with drusen volume included age (OR 1.42 per 5 years, 95% confidence interval [CI] 2.76, 4.48), systolic blood pressure (OR1.00, 95% CI 1.00, 1.01), ethnic Malay (OR 1.54, 95% CI 1.29, 1.83) and Chinese (OR 1.66, 95% CI 1.37, 2.01) compared to Indian. The ARMS2 rs10490924 T allele was associated with increased drusen volume in subjects with AMD (multivariable adjusted OR1.54, 95% CI 1.08, 2.19). Automated OCT-derived drusen volume is correlated with CFP-based AMD grading in many, but not all subjects. However the agreement is not good. These two modalities provide complementary information and should be incorporated into future studies.

Artificial intelligence in retina

Major advances in diagnostic technologies are offering unprecedented insight into the condition of the retina and beyond ocular disease. Digital images providing millions of morphological datasets can fast and non-invasively be analyzed in a comprehensive manner using artificial intelligence (AI). Methods based on machine learning (ML) and particularly deep learning (DL) are able to identify, localize and quantify pathological features in almost every macular and retinal disease. Convolutional neural networks thereby mimic the path of the human brain for object recognition through learning of pathological features from training sets, supervised ML, or even extrapolation from patterns recognized independently, unsupervised ML. The methods of AI-based retinal analyses are diverse and differ widely in their applicability, interpretability and reliability in different datasets and diseases. Fully automated AI-based systems have recently been approved for screening of diabetic retinopathy (DR). The overall potential of ML/DL includes screening, diagnostic grading as well as guidance of therapy with automated detection of disease activity, recurrences, quantification of therapeutic effects and identification of relevant targets for novel therapeutic approaches. Prediction and prognostic conclusions further expand the potential benefit of AI in retina which will enable personalized health care as well as large scale management and will empower the ophthalmologist to provide high quality diagnosis/therapy and successfully deal with the complexity of 21st century ophthalmology.

Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography

We developed a deep learning algorithm for the automatic segmentation and quantification of intraretinal cystoid fluid (IRC) in spectral domain optical coherence tomography (SD-OCT) volumes independent of the device used for acquisition. A cascade of neural networks was introduced to include prior information on the retinal anatomy, boosting performance significantly. The proposed algorithm approached human performance reaching an overall Dice coefficient of 0.754 ± 0.136 and an intraclass correlation coefficient of 0.936, for the task of IRC segmentation and quantification, respectively. The proposed method allows for fast quantitative IRC volume measurements that can be used to improve patient care, reduce costs, and allow fast and reliable analysis in large population studies.

Automated drusen detection in dry age-related macular degeneration by multiple-depth, en face optical coherence tomography

We introduce a method to automatically detect drusen in dry age-related macular degeneration (AMD) from optical coherence tomography with minimum need for layer segmentation. The method is based on the en face detection of drusen areas in C-scans at certain distances above the Bruch's membrane, circumventing the difficult task of pathologic retinal pigment epithelium segmentation. All types of drusen can be detected, including the challenging subretinal drusenoid deposits (pseudodrusen). The high sensitivity and accuracy demonstrated here shows its potential for detection of drusen onset in early AMD.