Automated retinal image quality assessment on the UK Biobank dataset for epidemiological studies.

Morphological changes in the retinal vascular network are associated with future risk of many systemic and vascular diseases. However, uncertainty over the presence and nature of some of these associations exists. Analysis of data from large population based studies will help to resolve these uncertainties. The QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) retinal image analysis system allows automated processing of large numbers of retinal images. However, an image quality assessment module is needed to achieve full automation. In this paper, we propose such an algorithm, which uses the segmented vessel map to determine the suitability of retinal images for use in the creation of vessel morphometric data suitable for epidemiological studies. This includes an effective 3-dimensional feature set and support vector machine classification. A random subset of 800 retinal images from UK Biobank (a large prospective study of 500,000 middle aged adults; where 68,151 underwent retinal imaging) was used to examine the performance of the image quality algorithm. The algorithm achieved a sensitivity of 95.33% and a specificity of 91.13% for the detection of inadequate images. The strong performance of this image quality algorithm will make rapid automated analysis of vascular morphometry feasible on the entire UK Biobank dataset (and other large retinal datasets), with minimal operator involvement, and at low cost

Automated quantification of retinal vessel morphometry in the UK Biobank Cohort

The following topics are dealt with: feature extraction; learning (artificial intelligence); image classification; image segmentation; computer vision; object detection; feedforward neural nets; image colour analysis; image representation; medical image processing

Optical Coherence Tomography in the UK Biobank Study - Rapid Automated Analysis of Retinal Thickness for Large Population-Based Studies

PURPOSE: To describe an approach to the use of optical coherence tomography (OCT) imaging in large, population-based studies, including methods for OCT image acquisition, storage, and the remote, rapid, automated analysis of retinal thickness. METHODS: In UK Biobank, OCT images were acquired between 2009 and 2010 using a commercially available “spectral domain” OCT device (3D OCT-1000, Topcon). Images were obtained using a raster scan protocol, 6 mm x 6 mm in area, and consisting of 128 B-scans. OCT image sets were stored on UK Biobank servers in a central repository, adjacent to high performance computers. Rapid, automated analysis of retinal thickness was performed using custom image segmentation software developed by the Topcon Advanced Biomedical Imaging Laboratory (TABIL). This software employs dual-scale gradient information to allow for automated segmentation of nine intraretinal boundaries in a rapid fashion. RESULTS: 67,321 participants (134,642 eyes) in UK Biobank underwent OCT imaging of both eyes as part of the ocular module. 134,611 images were successfully processed with 31 images failing segmentation analysis due to corrupted OCT files or withdrawal of subject consent for UKBB study participation. Average time taken to call up an image from the database and complete segmentation analysis was approximately 120 seconds per data set per login, and analysis of the entire dataset was completed in approximately 28 days. CONCLUSIONS: We report an approach to the rapid, automated measurement of retinal thickness from nearly 140,000 OCT image sets from the UK Biobank. In the near future, these measurements will be publically available for utilization by researchers around the world, and thus for correlation with the wealth of other data collected in UK Biobank. The automated analysis approaches we describe may be of utility for future large population-based epidemiological studies, clinical trials, and screening programs that employ OCT imaging

Optical Coherence Tomography in the UK Biobank Study – Rapid Automated Analysis of Retinal Thickness for Large Population-Based Studies

Purpose: To describe an approach to the use of optical coherence tomography (OCT) imaging in large, population-based studies, including methods for OCT image acquisition, storage, and the remote, rapid, automated analysis of retinal thickness. Methods: In UK Biobank, OCT images were acquired between 2009 and 2010 using a commercially available “spectral domain” OCT device (3D OCT-1000, Topcon). Images were obtained using a raster scan protocol, 6 mm x 6 mm in area, and consisting of 128 B-scans. OCT image sets were stored on UK Biobank servers in a central repository, adjacent to high performance computers. Rapid, automated analysis of retinal thickness was performed using custom image segmentation software developed by the Topcon Advanced Biomedical Imaging Laboratory (TABIL). This software employs dual-scale gradient information to allow for automated segmentation of nine intraretinal boundaries in a rapid fashion. Results: 67,321 participants (134,642 eyes) in UK Biobank underwent OCT imaging of both eyes as part of the ocular module. 134,611 images were successfully processed with 31 images failing segmentation analysis due to corrupted OCT files or withdrawal of subject consent for UKBB study participation. Average time taken to call up an image from the database and complete segmentation analysis was approximately 120 seconds per data set per login, and analysis of the entire dataset was completed in approximately 28 days. Conclusions: We report an approach to the rapid, automated measurement of retinal thickness from nearly 140,000 OCT image sets from the UK Biobank. In the near future, these measurements will be publically available for utilization by researchers around the world, and thus for correlation with the wealth of other data collected in UK Biobank. The automated analysis approaches we describe may be of utility for future large population-based epidemiological studies, clinical trials, and screening programs that employ OCT imaging

Cohort profile: rationale and methods of UK Biobank repeat imaging study eye measures to study dementia

PURPOSE: The retina provides biomarkers of neuronal and vascular health that offer promising insights into cognitive ageing, mild cognitive impairment and dementia. This article described the rationale and methodology of eye and vision assessments with the aim of supporting the study of dementia in the UK Biobank Repeat Imaging study. PARTICIPANTS: UK Biobank is a large-scale, multicentre, prospective cohort containing in-depth genetic, lifestyle, environmental and health information from half a million participants aged 40-69 enrolled in 2006-2010 across the UK. A subset (up to 60 000 participants) of the cohort will be invited to the UK Biobank Repeat Imaging Study to collect repeated brain, cardiac and abdominal MRI scans, whole-body dual-energy X-ray absorptiometry, carotid ultrasound, as well as retinal optical coherence tomography (OCT) and colour fundus photographs. FINDINGS TO DATE: UK Biobank has helped make significant advances in understanding risk factors for many common diseases, including for dementia and cognitive decline. Ophthalmic genetic and epidemiology studies have also benefited from the unparalleled combination of very large numbers of participants, deep phenotyping and longitudinal follow-up of the cohort, with comprehensive health data linkage to disease outcomes. In addition, we have used UK Biobank data to describe the relationship between retinal structures, cognitive function and brain MRI-derived phenotypes. FUTURE PLANS: The collection of eye-related data (eg, OCT), as part of the UK Biobank Repeat Imaging study, will take place in 2022-2028. The depth and breadth and longitudinal nature of this dataset, coupled with its open-access policy, will create a major new resource for dementia diagnostic discovery and to better understand its association with comorbid diseases. In addition, the broad and diverse data available in this study will support research into ophthalmic diseases and various other health outcomes beyond dementia

Cohort profile:rationale and methods of UK Biobank repeat imaging study eye measures to study dementia

Purpose: the retina provides biomarkers of neuronal and vascular health that offer promising insights into cognitive ageing, mild cognitive impairment and dementia. This article described the rationale and methodology of eye and vision assessments with the aim of supporting the study of dementia in the UK Biobank Repeat Imaging study.Participants: UK Biobank is a large-scale, multicentre, prospective cohort containing in-depth genetic, lifestyle, environmental and health information from half a million participants aged 40-69 enrolled in 2006-2010 across the UK. A subset (up to 60 000 participants) of the cohort will be invited to the UK Biobank Repeat Imaging Study to collect repeated brain, cardiac and abdominal MRI scans, whole-body dual-energy X-ray absorptiometry, carotid ultrasound, as well as retinal optical coherence tomography (OCT) and colour fundus photographs.Findings to date: UK Biobank has helped make significant advances in understanding risk factors for many common diseases, including for dementia and cognitive decline. Ophthalmic genetic and epidemiology studies have also benefited from the unparalleled combination of very large numbers of participants, deep phenotyping and longitudinal follow-up of the cohort, with comprehensive health data linkage to disease outcomes. In addition, we have used UK Biobank data to describe the relationship between retinal structures, cognitive function and brain MRI-derived phenotypes.Future plans: the collection of eye-related data (eg, OCT), as part of the UK Biobank Repeat Imaging study, will take place in 2022-2028. The depth and breadth and longitudinal nature of this dataset, coupled with its open-access policy, will create a major new resource for dementia diagnostic discovery and to better understand its association with comorbid diseases. In addition, the broad and diverse data available in this study will support research into ophthalmic diseases and various other health outcomes beyond dementia

Automated analysis of vessel morphometry in retinal images from a Danish high street optician setting.

PURPOSE: To evaluate the test performance of the QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) software in detecting retinal features from retinal images captured by health care professionals in a Danish high street optician chain, compared with test performance from other large population studies (i.e., UK Biobank) where retinal images were captured by non-experts. METHOD: The dataset FOREVERP (Finding Ophthalmic Risk and Evaluating the Value of Eye exams and their predictive Reliability, Pilot) contains retinal images obtained from a Danish high street optician chain. The QUARTZ algorithm utilizes both image processing and machine learning methods to determine retinal image quality, vessel segmentation, vessel width, vessel classification (arterioles or venules), and optic disc localization. Outcomes were evaluated by metrics including sensitivity, specificity, and accuracy and compared to human expert ground truths. RESULTS: QUARTZ's performance was evaluated on a subset of 3,682 images from the FOREVERP database. 80.55% of the FOREVERP images were labelled as being of adequate quality compared to 71.53% of UK Biobank images, with a vessel segmentation sensitivity of 74.64% and specificity of 98.41% (FOREVERP) compared with a sensitivity of 69.12% and specificity of 98.88% (UK Biobank). The mean (± standard deviation) vessel width of the ground truth was 16.21 (4.73) pixels compared to that predicted by QUARTZ of 17.01 (4.49) pixels, resulting in a difference of -0.8 (1.96) pixels. The differences were stable across a range of vessels. The detection rate for optic disc localisation was similar for the two datasets. CONCLUSION: QUARTZ showed high performance when evaluated on the FOREVERP dataset, and demonstrated robustness across datasets, providing validity to direct comparisons and pooling of retinal feature measures across data sources

AlzEye: longitudinal record-level linkage of ophthalmic imaging and hospital admissions of 353 157 patients in London, UK

PURPOSE: Retinal signatures of systemic disease ('oculomics') are increasingly being revealed through a combination of high-resolution ophthalmic imaging and sophisticated modelling strategies. Progress is currently limited not mainly by technical issues, but by the lack of large labelled datasets, a sine qua non for deep learning. Such data are derived from prospective epidemiological studies, in which retinal imaging is typically unimodal, cross-sectional, of modest number and relates to cohorts, which are not enriched with subpopulations of interest, such as those with systemic disease. We thus linked longitudinal multimodal retinal imaging from routinely collected National Health Service (NHS) data with systemic disease data from hospital admissions using a privacy-by-design third-party linkage approach. PARTICIPANTS: Between 1 January 2008 and 1 April 2018, 353 157 participants aged 40 years or older, who attended Moorfields Eye Hospital NHS Foundation Trust, a tertiary ophthalmic institution incorporating a principal central site, four district hubs and five satellite clinics in and around London, UK serving a catchment population of approximately six million people. FINDINGS TO DATE: Among the 353 157 individuals, 186 651 had a total of 1 337 711 Hospital Episode Statistics admitted patient care episodes. Systemic diagnoses recorded at these episodes include 12 022 patients with myocardial infarction, 11 735 with all-cause stroke and 13 363 with all-cause dementia. A total of 6 261 931 retinal images of seven different modalities and across three manufacturers were acquired from 1 54 830 patients. The majority of retinal images were retinal photographs (n=1 874 175) followed by optical coherence tomography (n=1 567 358). FUTURE PLANS: AlzEye combines the world's largest single institution retinal imaging database with nationally collected systemic data to create an exceptional large-scale, enriched cohort that reflects the diversity of the population served. First analyses will address cardiovascular diseases and dementia, with a view to identifying hidden retinal signatures that may lead to earlier detection and risk management of these life-threatening conditions