En-face optical coherence tomography in the diagnosis and management of age-related macular degeneration and polypoidal choroidal vasculopathy

published_or_final_versio

Replication and Refinement of an Algorithm for Automated Drusen Segmentation on Optical Coherence Tomography

Here, we investigate the extent to which re-implementing a previously published algorithm for OCT-based drusen quantification permits replicating the reported accuracy on an independent dataset. We refined that algorithm so that its accuracy is increased. Following a systematic literature search, an algorithm was selected based on its reported excellent results. Several steps were added to improve its accuracy. The replicated and refined algorithms were evaluated on an independent dataset with the same metrics as in the original publication. Accuracy of the refined algorithm (overlap ratio 36–52%) was significantly greater than the replicated one (overlap ratio 25–39%). In particular, separation of the retinal pigment epithelium and the ellipsoid zone could be improved by the refinement. However, accuracy was still lower than reported previously on different data (overlap ratio 67–76%). This is the first replication study of an algorithm for OCT image analysis. Its results indicate that current standards for algorithm validation do not provide a reliable estimate of algorithm performance on images that differ with respect to patient selection and image quality. In order to contribute to an improved reproducibility in this field, we publish both our replication and the refinement, as well as an exemplary dataset

Comparability of automated drusen volume measurements in age-related macular degeneration: a MACUSTAR study report

Drusen are hallmarks of early and intermediate age-related macular degeneration (AMD) but their quantification remains a challenge. We compared automated drusen volume measurements between different OCT devices. We included 380 eyes from 200 individuals with bilateral intermediate (iAMD, n = 126), early (eAMD, n = 25) or no AMD (n = 49) from the MACUSTAR study. We assessed OCT scans from Cirrus (200 × 200 macular cube, 6 × 6 mm; Zeiss Meditec, CA) and Spectralis (20° × 20°, 25 B-scans; 30° × 25°, 241 B-scans; Heidelberg Engineering, Germany) devices. Sensitivity and specificity for drusen detection and differences between modalities were assessed with intra-class correlation coefficients (ICCs) and mean difference in a 5 mm diameter fovea-centered circle. Specificity was > 90% in the three modalities. In eAMD, we observed highest sensitivity in the denser Spectralis scan (68.1). The two different Spectralis modalities showed a significantly higher agreement in quantifying drusen volume in iAMD (ICC 0.993 [0.991–0.994]) than the dense Spectralis with Cirrus scan (ICC 0.807 [0.757–0.847]). Formulae for drusen volume conversion in iAMD between the two devices are provided. Automated drusen volume measures are not interchangeable between devices and softwares and need to be interpreted with the used imaging devices and software in mind. Accounting for systematic difference between methods increases comparability and conversion formulae are provided. Less dense scans did not affect drusen volume measurements in iAMD but decreased sensitivity for medium drusen in eAMD. Trial registration: ClinicalTrials.gov NCT03349801. Registered on 22 November 2017

Comparability of automated drusen volume measurements in age-related macular degeneration: a MACUSTAR study report

Drusen are hallmarks of early and intermediate age-related macular degeneration (AMD) but their quantification remains a challenge. We compared automated drusen volume measurements between different OCT devices. We included 380 eyes from 200 individuals with bilateral intermediate (iAMD, n = 126), early (eAMD, n = 25) or no AMD (n = 49) from the MACUSTAR study. We assessed OCT scans from Cirrus (200 × 200 macular cube, 6 × 6 mm; Zeiss Meditec, CA) and Spectralis (20° × 20°, 25 B-scans; 30° × 25°, 241 B-scans; Heidelberg Engineering, Germany) devices. Sensitivity and specificity for drusen detection and differences between modalities were assessed with intra-class correlation coefficients (ICCs) and mean difference in a 5 mm diameter fovea-centered circle. Specificity was > 90% in the three modalities. In eAMD, we observed highest sensitivity in the denser Spectralis scan (68.1). The two different Spectralis modalities showed a significantly higher agreement in quantifying drusen volume in iAMD (ICC 0.993 [0.991–0.994]) than the dense Spectralis with Cirrus scan (ICC 0.807 [0.757–0.847]). Formulae for drusen volume conversion in iAMD between the two devices are provided. Automated drusen volume measures are not interchangeable between devices and softwares and need to be interpreted with the used imaging devices and software in mind. Accounting for systematic difference between methods increases comparability and conversion formulae are provided. Less dense scans did not affect drusen volume measurements in iAMD but decreased sensitivity for medium drusen in eAMD

Comparability of automated drusen volume measurements in age-related macular degeneration: a MACUSTAR study report

Drusen are hallmarks of early and intermediate age-related macular degeneration (AMD) but their quantification remains a challenge. We compared automated drusen volume measurements between different OCT devices. We included 380 eyes from 200 individuals with bilateral intermediate (iAMD, n = 126), early (eAMD, n = 25) or no AMD (n = 49) from the MACUSTAR study. We assessed OCT scans from Cirrus (200 × 200 macular cube, 6 × 6 mm; Zeiss Meditec, CA) and Spectralis (20° × 20°, 25 B-scans; 30° × 25°, 241 B-scans; Heidelberg Engineering, Germany) devices. Sensitivity and specificity for drusen detection and differences between modalities were assessed with intra-class correlation coefficients (ICCs) and mean difference in a 5 mm diameter fovea-centered circle. Specificity was > 90% in the three modalities. In eAMD, we observed highest sensitivity in the denser Spectralis scan (68.1). The two different Spectralis modalities showed a significantly higher agreement in quantifying drusen volume in iAMD (ICC 0.993 [0.991-0.994]) than the dense Spectralis with Cirrus scan (ICC 0.807 [0.757-0.847]). Formulae for drusen volume conversion in iAMD between the two devices are provided. Automated drusen volume measures are not interchangeable between devices and softwares and need to be interpreted with the used imaging devices and software in mind. Accounting for systematic difference between methods increases comparability and conversion formulae are provided. Less dense scans did not affect drusen volume measurements in iAMD but decreased sensitivity for medium drusen in eAMD

Automated Analysis of Retinal and Choroidal OCT and OCTA Images in AMD

La dégénérescence maculaire liée à l'âge (DMLA) est une maladie oculaire progressive qui se manifeste principalement au niveau de la rétine externe et de la choroïde. Le projet de recherche vise à déterminer si des mesures obtenues à partir d'images de tomographie par cohérence optique (OCT) et d'angiographie OCT (OCTA) peuvent être utilisées afin de fournir de nouvelles informations sur des biomarqueurs de la DMLA, ainsi qu’une méthode de détection précoce de la maladie. À cette fin, un appareil permettant l’OCT et l’OCTA a été utilisé pour imager des sujets DMLA précoces et intermédiaires, et des sujets témoins. À la configuration sélectionnée de l’appareil OCT, chaque acquisition d'un œil fournit un volume de données qui est constitué de 300 images transversales appelées B-scan. Au total, des acquisitions de 10 yeux de sujets atteints de DMLA précoce et intermédiaire (3000 images B-scan) et un cas de DMLA néovasculaire, 12 yeux de sujets âgés de plus de 50 ans (3600 images B-scan) et 11 yeux de sujets âgés de moins de 50 ans (3300 images B-scan) ont été obtenues. Cinq méthodes d'extraction de caractéristiques ont été reproduites ou développées afin de déterminer si des différences significatives au niveau de l’œil pouvaient être observées entre les sujets atteints de DMLA précoce et intermédiaire et les sujets témoins d’âge similaire. Grâce à des tests non paramétriques, il a été établi que deux méthodes connues d'extraction de biomarqueurs de la DMLA (analyse d’absence de signal de débit sanguin au niveau de la choriocapillaire et une méthode de segmentation des drusen) produisent des mesures qui montrent des différences significatives entre les groupes, et qui sont représentées de façon uniforme à travers le plan frontal de l’œil. Il a ensuite été souhaité de tirer parti des mesures et de générer un modèle de classification de la DMLA interprétable basé sur l'apprentissage automatique au niveau des B-scans. Des spectres de fréquence résultant de la transformé de Fourier rapide de séries spatiales dérivées de mesures considérées comme représentatives des deux biomarqueurs ont été obtenues, et utilisées comme caractéristiques pour former un classifieur de type forêt aléatoire et un classifieur de type forêt profonde. L'analyse en composantes principales (PCA) a été utilisée pour réduire la dimensionnalité de l’espace des caractéristiques, et la performance des modèles et l'importance des prédicteurs ont été évaluées. Une nouvelle méthode a été conçue qui permet une reconstruction 3D automatisée et une évaluation quantitative de la structure des signaux OCTA et ainsi des vaisseaux rétiniens. Des mesures représentatives des drusen et de la choriocapillaire ont été utilisées pour créer des modèles interprétables pour la classification de la DMLA précoce et intermédiaire. Alors que la prévalence mondiale de la DMLA augmente et que les appareils OCT deviennent plus disponibles, un plus grand nombre de personnes hautement qualifiées est nécessaire pour interpréter les informations médicales et fournir les soins cliniques appropriés. L'analyse et le classement du niveau de sévérité de la DMLA par des experts par le biais d'images OCT sont coûteux et prennent du temps. Les modèles proposés pourraient servir à automatiser la détection de la DMLA, même lorsqu'elle est asymptomatique, et signaler à un ophtalmologue la nécessité de surveiller et de traiter la condition avant la survenue de pertes graves de la vision. Les modèles sont transparents et sont en mesure de fournir une classification à partir d’une seule image transversale. Par conséquent, l'outil diagnostic automatisé pourrait également être utilisé dans des situations où seules des données médicales partielles sont disponibles ou lorsque l'accès aux ressources de soins de santé est limité.----------ABSTRACT Age-related macular degeneration (AMD) is a progressive eye disease which manifests primarily at the outer retina and choroid. The research project aimed to determine whether measures obtained from optical coherence tomography (OCT) and OCT angiography (OCTA) images could be used to provide novel AMD biomarker insight and an early disease detection method. To that end, an OCT and OCTA enabled device was used to image AMD subjects and controls. At the selected device scan size, each scan of one eye gathered using an OCT device provides a volume of data which is constructed of 300 cross-sectional images termed B-scans. In total, scans of 10 eyes from subjects with early and intermediate AMD (3,000 B-scan images) and a case of neovascular AMD, 12 eyes from subjects over the age of 50 years old (3,600 B-scan images), and 11 eyes from subjects under the age of 50 years old (3,300 B-scan images) were obtained. Five feature extraction methods were either reproduced or developed in order to determine if significant differences could be observed between the early and intermediate AMD subjects and control subjects at the eye level. Through non-parametric testing it was established that two AMD biomarker extraction methods (choriocapillaris flow voids analysis and a drusen segmentation method) produced measures which showed significant differences between groups, and which were also uniformly represented across the frontal plane of the eye. It was then desired to leverage the measures and generate a B-scan level, interpretable machine learning-based AMD classification model. Frequency spectrums resulting from the fast Fourier transforms of spatial series derived from measures believed to be representative of the two biomarkers were obtained and used as features to train a random forest and a deep forest classifier. Principal component analysis was used to reduce dimensionality of the feature space, and model performance and predictor importance were assessed. A new method was devised which allows automated 3D reconstruction and quantitative evaluation of retinal flow signal patterns and incidentally of retinal microvasculature. Measures representative of drusen and choriocapillaris were leveraged to create interpretable models for the classification of early and intermediate AMD. As the worldwide prevalence of AMD increases and OCT devices are becoming more available, a greater number of highly trained personnel is needed to interpret medical information and provide the appropriate clinical care. Expert analysis and grading of AMD through OCT images are expensive and time consuming. The models proposed could serve to automate AMD detection, even when it is asymptomatic, and signal to an ophthalmologist the need to monitor and treat the condition before the occurrence of severe visual loss. The models are transparent and provide classification from single cross-sectional images. Therefore, the automated diagnosis tool could also be used in situations where only partial medical data are available, or where there is limited access to health care resources

Identification of Surrogate Anatomic Identifiers of Disease Progression in Age-Related Macular Degeneration

Age-related macular degeneration (AMD) is the leading cause of vision loss in patients over 50 in the developed world. The visual impairment is due to either choroidal neovascularisation (wet AMD) or geographic atrophy (GA). Drusen is the hallmark of AMD but the presence of drusen does not inform progression to wet AMD. Although the disease is mostly bilateral, the rate of progression of disease in both eyes may not be simultaneous. If one eye is affected by wet AMD, the risk of progression of the fellow eye to wet AMD increases by 10% every year. However, there are no markers that inform the time of conversion to wet AMD. For this reason, there is an unmet need to identify biomarkers that can fully predict the progression to wet AMD in order to allow early intervention before permanent damage. My thesis aimed to assess whether changes in imaging characteristics can more precisely explain conversion. I studied various cohorts including (a) normal aging eyes (b) eyes with early/ intermediate AMD and (c) fellow eyes of unilateral wet AMD to study the conversion to wet AMD. Firstly, I evaluated longitudinally volume changes in inner and outer retinal layers of 71 eyes with early/intermediate AMD using optical coherence tomography (OCT). Our results showed that inner and outer retina layer volumes may differentiate AMD eyes from healthy eyes. When comparing those who progressed to wet AMD at year 2 to those who did not, we found that baseline volume of GCIPL may differentiate between the 2 groups. As it is an inner retinal change, I hypothesized that heritability of the retinal layers may influence the rate of retinal layer changes and that may in turn help understand the changes seen in aging and AMD. I worked with the TWIN Study database, in which OCT was done in eyes of twins of different age groups and OCT data were available on 364 eyes of 184 (92 pair) twins. I evaluated whether heritability was responsible for ageing changes of the retinal layers. I found that total retinal volume and inner retinal layer volumes may be affected by genetic factors