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Abtahi M, Le D, Ebrahimi B, Dadzie AK, Rahimi M, Hsieh YT, Heiferman MJ, Lim JI, Yao X. Differential artery-vein analysis improves the OCTA classification of diabetic retinopathy. BIOMEDICAL OPTICS EXPRESS 2024; 15:3889-3899. [PMID: 38867785 PMCID: PMC11166441 DOI: 10.1364/boe.521657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/25/2024] [Accepted: 05/14/2024] [Indexed: 06/14/2024]
Abstract
This study investigates the impact of differential artery-vein (AV) analysis in optical coherence tomography angiography (OCTA) on machine learning classification of diabetic retinopathy (DR). Leveraging deep learning for arterial-venous area (AVA) segmentation, six quantitative features, including perfusion intensity density (PID), blood vessel density (BVD), vessel area flux (VAF), blood vessel caliber (BVC), blood vessel tortuosity (BVT), and vessel perimeter index (VPI) features, were derived from OCTA images before and after AV differentiation. A support vector machine (SVM) classifier was utilized to assess both binary and multiclass classifications of control, diabetic patients without DR (NoDR), mild DR, moderate DR, and severe DR groups. Initially, one-region features, i.e., quantitative features extracted from the entire OCTA, were evaluated for DR classification. Differential AV analysis improved classification accuracies from 78.86% to 87.63% and from 79.62% to 85.66% for binary and multiclass classifications, respectively. Additionally, three-region features derived from the entire image, parafovea, and perifovea, were incorporated for DR classification. Differential AV analysis further enhanced classification accuracies from 84.43% to 93.33% and from 83.40% to 89.25% for binary and multiclass classifications, respectively. These findings highlight the potential of differential AV analysis in augmenting disease diagnosis and treatment assessment using OCTA.
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Affiliation(s)
- Mansour Abtahi
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - David Le
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Behrouz Ebrahimi
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Albert K. Dadzie
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Mojtaba Rahimi
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Yi-Ting Hsieh
- Department of Ophthalmology, National Taiwan University Hospital, Taipei, Taiwan
| | - Michael J. Heiferman
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Jennifer I. Lim
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Xincheng Yao
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA
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Yan S, Zhao J, She H, Jiang Y, Fan F, Yang G, Zhou J, Jia J, Zhang Y, Zhang L. Deep Learning based Retinal Vessel Caliber Measurement and the Association with Hypertension. Curr Eye Res 2024; 49:639-649. [PMID: 38407139 DOI: 10.1080/02713683.2024.2319755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 02/13/2024] [Indexed: 02/27/2024]
Abstract
PURPOSE To develop a highly efficient and fully automated method that measures retinal vessel caliber using digital retinal photographs and evaluate the association between retinal vessel caliber and hypertension. METHODS The subjects of this study were from two sources in Beijing, China, a hypertension case-control study from Tongren Hospital (Tongren study) and a community-based atherosclerosis cohort from Peking University First Hospital (Shougang study). Retinal vessel segmentation and arteriovenous classification were achieved simultaneously by a customized deep learning model. Two experienced ophthalmologists evaluated whether retinal vessels were correctly segmented and classified. The ratio of incorrectly segmented and classified retinal vessels was used to measure the accuracy of the model's recognition. Central retinal artery equivalents, central retinal vein equivalents and arteriolar-to-venular diameter ratio were computed to analyze the association between retinal vessel caliber and the risk of hypertension. The association was then compared to that derived from the widely used semi-automated software (Integrative Vessel Analysis). RESULTS The deep learning model achieved an arterial recognition error rate of 1.26%, a vein recognition error rate of 0.79%, and a total error rate of 1.03%. Central retinal artery equivalents and arteriolar-to-venular diameter ratio measured by both Integrative Vessel Analysis and deep learning methods were inversely associated with the odds of hypertension in both Tongren and Shougang studies. The comparisons of areas under the receiver operating characteristic curves from the proposed deep learning method and Integrative Vessel Analysis were all not significantly different (p > .05). CONCLUSION The proposed deep learning method showed a comparable diagnostic value to Integrative Vessel Analysis software. Compared with semi-automatic software, our deep learning model has significant advantage in efficiency and can be applied to population screening and risk evaluation.
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Affiliation(s)
- Shenshen Yan
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology and Visual Science Key Lab, Beijing, China
| | - Jie Zhao
- National Engineering Laboratory for Big Data Analysis and Applications, Peking University, Beijing, China
| | - Haicheng She
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology and Visual Science Key Lab, Beijing, China
| | - Yimeng Jiang
- Department of Cardiology, Peking University First Hospital, Beijing, China
| | - Fangfang Fan
- Department of Cardiology, Peking University First Hospital, Beijing, China
- Institute of Cardiovascular Disease, Peking University First Hospital, Beijing, China
| | | | - Jinqiong Zhou
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology and Visual Science Key Lab, Beijing, China
| | - Jia Jia
- Department of Cardiology, Peking University First Hospital, Beijing, China
- Institute of Cardiovascular Disease, Peking University First Hospital, Beijing, China
| | - Yan Zhang
- Department of Cardiology, Peking University First Hospital, Beijing, China
- Institute of Cardiovascular Disease, Peking University First Hospital, Beijing, China
| | - Li Zhang
- Center for Data Science, Peking University, Beijing, China
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Fhima J, Van Eijgen J, Billen Moulin-Romsée MI, Brackenier H, Kulenovic H, Debeuf V, Vangilbergen M, Freiman M, Stalmans I, Behar JA. LUNet: deep learning for the segmentation of arterioles and venules in high resolution fundus images. Physiol Meas 2024; 45:055002. [PMID: 38599224 DOI: 10.1088/1361-6579/ad3d28] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 04/10/2024] [Indexed: 04/12/2024]
Abstract
Objective.This study aims to automate the segmentation of retinal arterioles and venules (A/V) from digital fundus images (DFI), as changes in the spatial distribution of retinal microvasculature are indicative of cardiovascular diseases, positioning the eyes as windows to cardiovascular health.Approach.We utilized active learning to create a new DFI dataset with 240 crowd-sourced manual A/V segmentations performed by 15 medical students and reviewed by an ophthalmologist. We then developed LUNet, a novel deep learning architecture optimized for high-resolution A/V segmentation. The LUNet model features a double dilated convolutional block to widen the receptive field and reduce parameter count, alongside a high-resolution tail to refine segmentation details. A custom loss function was designed to prioritize the continuity of blood vessel segmentation.Main Results.LUNet significantly outperformed three benchmark A/V segmentation algorithms both on a local test set and on four external test sets that simulated variations in ethnicity, comorbidities and annotators.Significance.The release of the new datasets and the LUNet model (www.aimlab-technion.com/lirot-ai) provides a valuable resource for the advancement of retinal microvasculature analysis. The improvements in A/V segmentation accuracy highlight LUNet's potential as a robust tool for diagnosing and understanding cardiovascular diseases through retinal imaging.
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Affiliation(s)
- Jonathan Fhima
- Faculty of Biomedical Engineering, Technion-IIT, Haifa, Israel
- Department of Applied Mathematics, Technion-IIT, Haifa, Israel
| | - Jan Van Eijgen
- Research Group of Ophthalmology, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Department of Ophthalmology, University Hospitals UZ Leuven, Leuven, Belgium
| | - Marie-Isaline Billen Moulin-Romsée
- Research Group of Ophthalmology, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Department of Ophthalmology, University Hospitals UZ Leuven, Leuven, Belgium
| | - Heloïse Brackenier
- Research Group of Ophthalmology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Hana Kulenovic
- Research Group of Ophthalmology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Valérie Debeuf
- Research Group of Ophthalmology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Marie Vangilbergen
- Research Group of Ophthalmology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Moti Freiman
- Faculty of Biomedical Engineering, Technion-IIT, Haifa, Israel
| | - Ingeborg Stalmans
- Research Group of Ophthalmology, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Department of Ophthalmology, University Hospitals UZ Leuven, Leuven, Belgium
| | - Joachim A Behar
- Faculty of Biomedical Engineering, Technion-IIT, Haifa, Israel
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Chen Q, Peng J, Zhao S, Liu W. Automatic artery/vein classification methods for retinal blood vessel: A review. Comput Med Imaging Graph 2024; 113:102355. [PMID: 38377630 DOI: 10.1016/j.compmedimag.2024.102355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 02/06/2024] [Accepted: 02/06/2024] [Indexed: 02/22/2024]
Abstract
Automatic retinal arteriovenous classification can assist ophthalmologists in disease early diagnosis. Deep learning-based methods and topological graph-based methods have become the main solutions for retinal arteriovenous classification in recent years. This paper reviews the automatic retinal arteriovenous classification methods from 2003 to 2022. Firstly, we compare different methods and provide comparison tables of the summary results. Secondly, we complete the classification of the public arteriovenous classification datasets and provide the annotation development tables of different datasets. Finally, we sort out the challenges of evaluation methods and provide a comprehensive evaluation system. Quantitative and qualitative analysis shows the changes in research hotspots over time, Quantitative and qualitative analyses reveal the evolution of research hotspots over time, highlighting the significance of exploring the integration of deep learning with topological information in future research.
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Affiliation(s)
- Qihan Chen
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Jianqing Peng
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; Guangdong Provincial Key Laboratory of Fire Science and Technology, Guangzhou 510006, China.
| | - Shen Zhao
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
| | - Wanquan Liu
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
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Zhou Y, Xu M, Hu Y, Blumberg SB, Zhao A, Wagner SK, Keane PA, Alexander DC. CF-Loss: Clinically-relevant feature optimised loss function for retinal multi-class vessel segmentation and vascular feature measurement. Med Image Anal 2024; 93:103098. [PMID: 38320370 DOI: 10.1016/j.media.2024.103098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 05/22/2023] [Accepted: 01/30/2024] [Indexed: 02/08/2024]
Abstract
Characterising clinically-relevant vascular features, such as vessel density and fractal dimension, can benefit biomarker discovery and disease diagnosis for both ophthalmic and systemic diseases. In this work, we explicitly encode vascular features into an end-to-end loss function for multi-class vessel segmentation, categorising pixels into artery, vein, uncertain pixels, and background. This clinically-relevant feature optimised loss function (CF-Loss) regulates networks to segment accurate multi-class vessel maps that produce precise vascular features. Our experiments first verify that CF-Loss significantly improves both multi-class vessel segmentation and vascular feature estimation, with two standard segmentation networks, on three publicly available datasets. We reveal that pixel-based segmentation performance is not always positively correlated with accuracy of vascular features, thus highlighting the importance of optimising vascular features directly via CF-Loss. Finally, we show that improved vascular features from CF-Loss, as biomarkers, can yield quantitative improvements in the prediction of ischaemic stroke, a real-world clinical downstream task. The code is available at https://github.com/rmaphoh/feature-loss.
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Affiliation(s)
- Yukun Zhou
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK; NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London EC1V 9EL, UK; Institute of Ophthalmology, University College London, London EC1V 9EL, UK.
| | - MouCheng Xu
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK; Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK
| | - Yipeng Hu
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK; Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TS, UK
| | - Stefano B Blumberg
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK; Department of Computer Science, University College London, London WC1E 6BT, UK
| | - An Zhao
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK; Department of Computer Science, University College London, London WC1E 6BT, UK
| | - Siegfried K Wagner
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London EC1V 9EL, UK; Institute of Ophthalmology, University College London, London EC1V 9EL, UK
| | - Pearse A Keane
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London EC1V 9EL, UK; Institute of Ophthalmology, University College London, London EC1V 9EL, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK; Department of Computer Science, University College London, London WC1E 6BT, UK
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Abbas Q, Daadaa Y, Rashid U, Sajid MZ, Ibrahim MEA. HDR-EfficientNet: A Classification of Hypertensive and Diabetic Retinopathy Using Optimize EfficientNet Architecture. Diagnostics (Basel) 2023; 13:3236. [PMID: 37892058 PMCID: PMC10606674 DOI: 10.3390/diagnostics13203236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 10/03/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023] Open
Abstract
Hypertensive retinopathy (HR) and diabetic retinopathy (DR) are retinal diseases closely associated with high blood pressure. The severity and duration of hypertension directly impact the prevalence of HR. The early identification and assessment of HR are crucial to preventing blindness. Currently, limited computer-aided methods are available for detecting HR and DR. These existing systems rely on traditional machine learning approaches, which require complex image processing techniques and are often limited in their application. To address this challenge, this work introduces a deep learning (DL) method called HDR-EfficientNet, which aims to provide an efficient and accurate approach to identifying various eye-related disorders, including diabetes and hypertensive retinopathy. The proposed method utilizes an EfficientNet-V2 network for end-to-end training focused on disease classification. Additionally, a spatial-channel attention method is incorporated into the approach to enhance its ability to identify specific areas of damage and differentiate between different illnesses. The HDR-EfficientNet model is developed using transfer learning, which helps overcome the challenge of imbalanced sample classes and improves the network's generalization. Dense layers are added to the model structure to enhance the feature selection capacity. The performance of the implemented system is evaluated using a large dataset of over 36,000 augmented retinal fundus images. The results demonstrate promising accuracy, with an average area under the curve (AUC) of 0.98, a specificity (SP) of 96%, an accuracy (ACC) of 98%, and a sensitivity (SE) of 95%. These findings indicate the effectiveness of the suggested HDR-EfficientNet classifier in diagnosing HR and DR. In summary, the HDR-EfficientNet method presents a DL-based approach that offers improved accuracy and efficiency for the detection and classification of HR and DR, providing valuable support in diagnosing and managing these eye-related conditions.
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Affiliation(s)
- Qaisar Abbas
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (Y.D.)
| | - Yassine Daadaa
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (Y.D.)
| | - Umer Rashid
- Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan;
| | - Muhammad Zaheer Sajid
- Department of Computer Software Engineering, MCS, National University of Science and Technology, Islamabad 44000, Pakistan
| | - Mostafa E. A. Ibrahim
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (Y.D.)
- Department of Electrical Engineering, Benha Faculty of Engineering, Benha University, Benha 13518, Qalubia, Egypt
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Suman S, Tiwari AK, Singh K. Computer-aided diagnostic system for hypertensive retinopathy: A review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107627. [PMID: 37320942 DOI: 10.1016/j.cmpb.2023.107627] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 05/03/2023] [Accepted: 05/27/2023] [Indexed: 06/17/2023]
Abstract
Hypertensive Retinopathy (HR) is a retinal disease caused by elevated blood pressure for a prolonged period. There are no obvious signs in the early stages of high blood pressure, but it affects various body parts over time, including the eyes. HR is a biomarker for several illnesses, including retinal diseases, atherosclerosis, strokes, kidney disease, and cardiovascular risks. Early microcirculation abnormalities in chronic diseases can be diagnosed through retinal examination prior to the onset of major clinical consequences. Computer-aided diagnosis (CAD) plays a vital role in the early identification of HR with improved diagnostic accuracy, which is time-efficient and demands fewer resources. Recently, numerous studies have been reported on the automatic identification of HR. This paper provides a comprehensive review of the automated tasks of Artery-Vein (A/V) classification, Arteriovenous ratio (AVR) computation, HR detection (Binary classification), and HR severity grading. The review is conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. The paper discusses the clinical features of HR, the availability of datasets, existing methods used for A/V classification, AVR computation, HR detection, and severity grading, and performance evaluation metrics. The reviewed articles are summarized with classifiers details, adoption of different kinds of methodologies, performance comparisons, datasets details, their pros and cons, and computational platform. For each task, a summary and critical in-depth analysis are provided, as well as common research issues and challenges in the existing studies. Finally, the paper proposes future research directions to overcome challenges associated with data set availability, HR detection, and severity grading.
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Affiliation(s)
- Supriya Suman
- Interdisciplinary Research Platform (IDRP): Smart Healthcare, Indian Institute of Technology, N.H. 62, Nagaur Road, Karwar, Jodhpur, Rajasthan 342030, India.
| | - Anil Kumar Tiwari
- Department of Electrical Engineering, Indian Institute of Technology, N.H. 62, Nagaur Road, Karwar, Jodhpur, Rajasthan 342030, India
| | - Kuldeep Singh
- Department of Pediatrics, All India Institute of Medical Sciences, Basni Industrial Area Phase-2, Jodhpur, Rajasthan 342005, India
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Freiberg J, Welikala RA, Rovelt J, Owen CG, Rudnicka AR, Kolko M, Barman SA. Automated analysis of vessel morphometry in retinal images from a Danish high street optician setting. PLoS One 2023; 18:e0290278. [PMID: 37616264 PMCID: PMC10449151 DOI: 10.1371/journal.pone.0290278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 06/29/2023] [Indexed: 08/26/2023] Open
Abstract
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.
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Affiliation(s)
- Josefine Freiberg
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Roshan A. Welikala
- School of Computer Science and Mathematics, Kingston University, Surrey, United Kingdom
| | - Jens Rovelt
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Christopher G. Owen
- Population Health Research Institute, St. George’s, University of London, London, United Kingdom
| | - Alicja R. Rudnicka
- Population Health Research Institute, St. George’s, University of London, London, United Kingdom
| | - Miriam Kolko
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
- Department of Ophthalmology, Copenhagen University Hospital, Rigshospitalet, Glostrup, Copenhagen, Denmark
| | - Sarah A. Barman
- School of Computer Science and Mathematics, Kingston University, Surrey, United Kingdom
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Foster PJ, Atan D, Khawaja A, Lotery A, MacGillivray T, Owen CG, Patel PJ, Petzold A, Rudnicka A, Sun Z, Sheard S, Allen N. Cohort profile: rationale and methods of UK Biobank repeat imaging study eye measures to study dementia. BMJ Open 2023; 13:e069258. [PMID: 37355273 PMCID: PMC10314584 DOI: 10.1136/bmjopen-2022-069258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 05/22/2023] [Indexed: 06/26/2023] Open
Abstract
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.
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Affiliation(s)
- Paul J Foster
- Moorfields Eye Hospital NHS Foundation Trust, NIHR Moorfields Biomedical Research Centre, London, UK
| | - Denize Atan
- Medical School, University of Bristol, Bristol, UK
| | - Anthony Khawaja
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust & UCL Institute of Ophthalmology, London, UK
| | - Andrew Lotery
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - Tom MacGillivray
- Clinical Research Imaging Centre, Queens Medical Research Institution, Edinburgh, UK
| | - Christopher G Owen
- Population Health Research Institute, St Georges Medical School, University of London, London, UK
| | - Praveen J Patel
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Axel Petzold
- Department of Molecular Neurosciences, Moorfields Eye Hospital and The National Hospital for Neurology and Neurosurgery, Queen Square Institute of Neurology, UCL, London, UK
- Departments of Neurology, Ophthalmology and Expertise Center for Neuro-ophthalmology, Amsterdam UMC, Amsterdam, The Netherlands
| | - Alicja Rudnicka
- Population Health Research Institute, St Georges Medical School, University of London, London, UK
| | - Zihan Sun
- Institute of Ophthalmology, University College London, London, UK
| | | | - Naomi Allen
- UK Biobank, Stockport, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
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Abtahi M, Le D, Ebrahimi B, Dadzie AK, Lim JI, Yao X. An open-source deep learning network AVA-Net for arterial-venous area segmentation in optical coherence tomography angiography. COMMUNICATIONS MEDICINE 2023; 3:54. [PMID: 37069396 PMCID: PMC10110614 DOI: 10.1038/s43856-023-00287-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 04/06/2023] [Indexed: 04/19/2023] Open
Abstract
BACKGROUND Differential artery-vein (AV) analysis in optical coherence tomography angiography (OCTA) holds promise for the early detection of eye diseases. However, currently available methods for AV analysis are limited for binary processing of retinal vasculature in OCTA, without quantitative information of vascular perfusion intensity. This study is to develop and validate a method for quantitative AV analysis of vascular perfusion intensity. METHOD A deep learning network AVA-Net has been developed for automated AV area (AVA) segmentation in OCTA. Seven new OCTA features, including arterial area (AA), venous area (VA), AVA ratio (AVAR), total perfusion intensity density (T-PID), arterial PID (A-PID), venous PID (V-PID), and arterial-venous PID ratio (AV-PIDR), were extracted and tested for early detection of diabetic retinopathy (DR). Each of these seven features was evaluated for quantitative evaluation of OCTA images from healthy controls, diabetic patients without DR (NoDR), and mild DR. RESULTS It was observed that the area features, i.e., AA, VA and AVAR, can reveal significant differences between the control and mild DR. Vascular perfusion parameters, including T-PID and A-PID, can differentiate mild DR from control group. AV-PIDR can disclose significant differences among all three groups, i.e., control, NoDR, and mild DR. According to Bonferroni correction, the combination of A-PID and AV-PIDR can reveal significant differences in all three groups. CONCLUSIONS AVA-Net, which is available on GitHub for open access, enables quantitative AV analysis of AV area and vascular perfusion intensity. Comparative analysis revealed AV-PIDR as the most sensitive feature for OCTA detection of early DR. Ensemble AV feature analysis, e.g., the combination of A-PID and AV-PIDR, can further improve the performance for early DR assessment.
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Affiliation(s)
- Mansour Abtahi
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, 60607, USA
| | - David Le
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, 60607, USA
| | - Behrouz Ebrahimi
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, 60607, USA
| | - Albert K Dadzie
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, 60607, USA
| | - Jennifer I Lim
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL, 60612, USA
| | - Xincheng Yao
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, 60607, USA.
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL, 60612, USA.
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11
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Arnould L, Meriaudeau F, Guenancia C, Germanese C, Delcourt C, Kawasaki R, Cheung CY, Creuzot-Garcher C, Grzybowski A. Using Artificial Intelligence to Analyse the Retinal Vascular Network: The Future of Cardiovascular Risk Assessment Based on Oculomics? A Narrative Review. Ophthalmol Ther 2023; 12:657-674. [PMID: 36562928 PMCID: PMC10011267 DOI: 10.1007/s40123-022-00641-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 12/09/2022] [Indexed: 12/24/2022] Open
Abstract
The healthcare burden of cardiovascular diseases remains a major issue worldwide. Understanding the underlying mechanisms and improving identification of people with a higher risk profile of systemic vascular disease through noninvasive examinations is crucial. In ophthalmology, retinal vascular network imaging is simple and noninvasive and can provide in vivo information of the microstructure and vascular health. For more than 10 years, different research teams have been working on developing software to enable automatic analysis of the retinal vascular network from different imaging techniques (retinal fundus photographs, OCT angiography, adaptive optics, etc.) and to provide a description of the geometric characteristics of its arterial and venous components. Thus, the structure of retinal vessels could be considered a witness of the systemic vascular status. A new approach called "oculomics" using retinal image datasets and artificial intelligence algorithms recently increased the interest in retinal microvascular biomarkers. Despite the large volume of associated research, the role of retinal biomarkers in the screening, monitoring, or prediction of systemic vascular disease remains uncertain. A PubMed search was conducted until August 2022 and yielded relevant peer-reviewed articles based on a set of inclusion criteria. This literature review is intended to summarize the state of the art in oculomics and cardiovascular disease research.
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Affiliation(s)
- Louis Arnould
- Ophthalmology Department, Dijon University Hospital, 14 Rue Paul Gaffarel, 21079, Dijon CEDEX, France. .,University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR U1219, 33000, Bordeaux, France.
| | - Fabrice Meriaudeau
- Laboratory ImViA, IFTIM, Université Bourgogne Franche-Comté, 21078, Dijon, France
| | - Charles Guenancia
- Pathophysiology and Epidemiology of Cerebro-Cardiovascular Diseases, (EA 7460), Faculty of Health Sciences, Université de Bourgogne Franche-Comté, Dijon, France.,Cardiology Department, Dijon University Hospital, Dijon, France
| | - Clément Germanese
- Ophthalmology Department, Dijon University Hospital, 14 Rue Paul Gaffarel, 21079, Dijon CEDEX, France
| | - Cécile Delcourt
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR U1219, 33000, Bordeaux, France
| | - Ryo Kawasaki
- Artificial Intelligence Center for Medical Research and Application, Osaka University Hospital, Osaka, Japan
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Catherine Creuzot-Garcher
- Ophthalmology Department, Dijon University Hospital, 14 Rue Paul Gaffarel, 21079, Dijon CEDEX, France.,Centre des Sciences du Goût et de l'Alimentation, AgroSup Dijon, CNRS, INRAE, Université Bourgogne Franche-Comté, Dijon, France
| | - Andrzej Grzybowski
- Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland.,Institute for Research in Ophthalmology, Poznan, Poland
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12
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End-to-End Automatic Classification of Retinal Vessel Based on Generative Adversarial Networks with Improved U-Net. Diagnostics (Basel) 2023; 13:diagnostics13061148. [PMID: 36980456 PMCID: PMC10047448 DOI: 10.3390/diagnostics13061148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 03/07/2023] [Accepted: 03/13/2023] [Indexed: 03/19/2023] Open
Abstract
The retinal vessels in the human body are the only ones that can be observed directly by non-invasive imaging techniques. Retinal vessel morphology and structure are the important objects of concern for physicians in the early diagnosis and treatment of related diseases. The classification of retinal vessels has important guiding significance in the basic stage of diagnostic treatment. This paper proposes a novel method based on generative adversarial networks with improved U-Net, which can achieve synchronous automatic segmentation and classification of blood vessels by an end-to-end network. The proposed method avoids the dependency of the segmentation results in the multiple classification tasks. Moreover, the proposed method builds on an accurate classification of arteries and veins while also classifying arteriovenous crossings. The validity of the proposed method is evaluated on the RITE dataset: the accuracy of image comprehensive classification reaches 96.87%. The sensitivity and specificity of arteriovenous classification reach 91.78% and 97.25%. The results verify the effectiveness of the proposed method and show the competitive classification performance.
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13
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Abstract
The eye is the window through which light is transmitted and visual sensory signalling originates. It is also a window through which elements of the cardiovascular and nervous systems can be directly inspected, using ophthalmoscopy or retinal imaging. Measurements of ocular parameters may therefore offer important information on the physiology and homeostasis of these two important systems. Here we report the results of a genetic characterisation of retinal vasculature. Four genome-wide association studies performed on different aspects of retinal vasculometry phenotypes, such as arteriolar and venular tortuosity and width, found significant similarities between retinal vascular characteristics and cardiometabolic health. Our analyses identified 119 different regions of association with traits of retinal vasculature, including 89 loci associated arteriolar tortuosity, the strongest of which was rs35131825 (p = 2.00×10-108), 2 loci with arteriolar width (rs12969347, p = 3.30×10-09 and rs5442, p = 1.9E-15), 17 other loci associated with venular tortuosity and 11 novel associations with venular width. Our causal inference analyses also found that factors linked to arteriolar tortuosity cause elevated diastolic blood pressure and not vice versa.
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14
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Rudnicka AR, Welikala R, Barman S, Foster PJ, Luben R, Hayat S, Khaw KT, Whincup P, Strachan D, Owen CG. Artificial intelligence-enabled retinal vasculometry for prediction of circulatory mortality, myocardial infarction and stroke. Br J Ophthalmol 2022; 106:1722-1729. [PMID: 36195457 PMCID: PMC9685715 DOI: 10.1136/bjo-2022-321842] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/03/2022] [Indexed: 02/02/2023]
Abstract
AIMS We examine whether inclusion of artificial intelligence (AI)-enabled retinal vasculometry (RV) improves existing risk algorithms for incident stroke, myocardial infarction (MI) and circulatory mortality. METHODS AI-enabled retinal vessel image analysis processed images from 88 052 UK Biobank (UKB) participants (aged 40-69 years at image capture) and 7411 European Prospective Investigation into Cancer (EPIC)-Norfolk participants (aged 48-92). Retinal arteriolar and venular width, tortuosity and area were extracted. Prediction models were developed in UKB using multivariable Cox proportional hazards regression for circulatory mortality, incident stroke and MI, and externally validated in EPIC-Norfolk. Model performance was assessed using optimism adjusted calibration, C-statistics and R2 statistics. Performance of Framingham risk scores (FRS) for incident stroke and incident MI, with addition of RV to FRS, were compared with a simpler model based on RV, age, smoking status and medical history (antihypertensive/cholesterol lowering medication, diabetes, prevalent stroke/MI). RESULTS UKB prognostic models were developed on 65 144 participants (mean age 56.8; median follow-up 7.7 years) and validated in 5862 EPIC-Norfolk participants (67.6, 9.1 years, respectively). Prediction models for circulatory mortality in men and women had optimism adjusted C-statistics and R2 statistics between 0.75-0.77 and 0.33-0.44, respectively. For incident stroke and MI, addition of RV to FRS did not improve model performance in either cohort. However, the simpler RV model performed equally or better than FRS. CONCLUSION RV offers an alternative predictive biomarker to traditional risk-scores for vascular health, without the need for blood sampling or blood pressure measurement. Further work is needed to examine RV in population screening to triage individuals at high-risk.
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Affiliation(s)
| | - Roshan Welikala
- Faculty of Science, Engineering and Computing, Kingston University, Kingston-Upon-Thames, UK
| | - Sarah Barman
- Faculty of Science, Engineering and Computing, Kingston University, Kingston-Upon-Thames, UK
| | - Paul J Foster
- NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, University College London, London, UK
| | - Robert Luben
- MRC Epidemiology Unit, Cambridge University, Cambridge, UK
| | - Shabina Hayat
- Department of Psychiatry, Cambridge Public Health, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Kay-Tee Khaw
- MRC Epidemiology Unit, Cambridge University, Cambridge, UK
| | - Peter Whincup
- Population Health Research Institute, St George's University of London, London, UK
| | - David Strachan
- Population Health Research Institute, St George's University of London, London, UK
| | - Christopher G Owen
- Population Health Research Institute, St George's University of London, London, UK
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15
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Toptaş B, Hanbay D. Separation of arteries and veins in retinal fundus images with a new CNN architecture. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2022. [DOI: 10.1080/21681163.2022.2151066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Buket Toptaş
- Computer Engineering Department, Engineering and Natural Science Faculty, Bandırma Onyedi Eylül University, Balıkesir, Turkey
| | - Davut Hanbay
- Computer Engineering Department, Engineering Faculty, Inonu University, Malatya, Turkey
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16
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Chowdhury AZME, Mann G, Morgan WH, Vukmirovic A, Mehnert A, Sohel F. MSGANet-RAV: A multiscale guided attention network for artery-vein segmentation and classification from optic disc and retinal images. JOURNAL OF OPTOMETRY 2022; 15 Suppl 1:S58-S69. [PMID: 36396540 PMCID: PMC9732479 DOI: 10.1016/j.optom.2022.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 09/23/2022] [Accepted: 11/06/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Retinal and optic disc images are used to assess changes in the retinal vasculature. These can be changes associated with diseases such as diabetic retinopathy and glaucoma or induced using ophthalmodynamometry to measure arterial and venous pressure. Key steps toward automating the assessment of these changes are the segmentation and classification of the veins and arteries. However, such segmentation and classification are still required to be manually labelled by experts. Such automated labelling is challenging because of the complex morphology, anatomical variations, alterations due to disease and scarcity of labelled data for algorithm development. We present a deep machine learning solution called the multiscale guided attention network for retinal artery and vein segmentation and classification (MSGANet-RAV). METHODS MSGANet-RAV was developed and tested on 383 colour clinical optic disc images from LEI-CENTRAL, constructed in-house and 40 colour fundus images from the AV-DRIVE public dataset. The datasets have a mean optic disc occupancy per image of 60.6% and 2.18%, respectively. MSGANet-RAV is a U-shaped encoder-decoder network, where the encoder extracts multiscale features, and the decoder includes a sequence of self-attention modules. The self-attention modules explore, guide and incorporate vessel-specific structural and contextual feature information to segment and classify central optic disc and retinal vessel pixels. RESULTS MSGANet-RAV achieved a pixel classification accuracy of 93.15%, sensitivity of 92.19%, and specificity of 94.13% on LEI-CENTRAL, outperforming several reference models. It similarly performed highly on AV-DRIVE with an accuracy, sensitivity and specificity of 95.48%, 93.59% and 97.27%, respectively. CONCLUSION The results show the efficacy of MSGANet-RAV for identifying central optic disc and retinal arteries and veins. The method can be used in automated systems designed to assess vascular changes in retinal and optic disc images quantitatively.
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Affiliation(s)
- A Z M Ehtesham Chowdhury
- School of Information Technology, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia
| | - Graham Mann
- School of Information Technology, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia
| | - William Huxley Morgan
- Lions Eye Institute, 2 Verdun Street, Nedlands, WA 6009, Australia; Centre for Ophthalmology and Visual Science, The University of Western Australia, 35 Stirling Highway, Perth, WA 6009, Australia
| | - Aleksandar Vukmirovic
- Lions Eye Institute, 2 Verdun Street, Nedlands, WA 6009, Australia; Centre for Ophthalmology and Visual Science, The University of Western Australia, 35 Stirling Highway, Perth, WA 6009, Australia
| | - Andrew Mehnert
- Lions Eye Institute, 2 Verdun Street, Nedlands, WA 6009, Australia; Centre for Ophthalmology and Visual Science, The University of Western Australia, 35 Stirling Highway, Perth, WA 6009, Australia
| | - Ferdous Sohel
- School of Information Technology, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia.
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17
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Rodrigues EO, Rodrigues LO, Machado JHP, Casanova D, Teixeira M, Oliva JT, Bernardes G, Liatsis P. Local-Sensitive Connectivity Filter (LS-CF): A Post-Processing Unsupervised Improvement of the Frangi, Hessian and Vesselness Filters for Multimodal Vessel Segmentation. J Imaging 2022; 8:jimaging8100291. [PMID: 36286385 PMCID: PMC9604711 DOI: 10.3390/jimaging8100291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/13/2022] [Accepted: 09/16/2022] [Indexed: 11/07/2022] Open
Abstract
A retinal vessel analysis is a procedure that can be used as an assessment of risks to the eye. This work proposes an unsupervised multimodal approach that improves the response of the Frangi filter, enabling automatic vessel segmentation. We propose a filter that computes pixel-level vessel continuity while introducing a local tolerance heuristic to fill in vessel discontinuities produced by the Frangi response. This proposal, called the local-sensitive connectivity filter (LS-CF), is compared against a naive connectivity filter to the baseline thresholded Frangi filter response and to the naive connectivity filter response in combination with the morphological closing and to the current approaches in the literature. The proposal was able to achieve competitive results in a variety of multimodal datasets. It was robust enough to outperform all the state-of-the-art approaches in the literature for the OSIRIX angiographic dataset in terms of accuracy and 4 out of 5 works in the case of the IOSTAR dataset while also outperforming several works in the case of the DRIVE and STARE datasets and 6 out of 10 in the CHASE-DB dataset. For the CHASE-DB, it also outperformed all the state-of-the-art unsupervised methods.
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Affiliation(s)
- Erick O. Rodrigues
- Department of Academic Informatics (DAINF), Universidade Tecnologica Federal do Parana (UTFPR), Pato Branco 85503-390, PR, Brazil
- Correspondence:
| | - Lucas O. Rodrigues
- Graduate Program of Sciences Applied to Health Products, Universidade Federal Fluminense (UFF), Niteroi 24241-000, RJ, Brazil
| | - João H. P. Machado
- Department of Academic Informatics (DAINF), Universidade Tecnologica Federal do Parana (UTFPR), Pato Branco 85503-390, PR, Brazil
| | - Dalcimar Casanova
- Department of Academic Informatics (DAINF), Universidade Tecnologica Federal do Parana (UTFPR), Pato Branco 85503-390, PR, Brazil
| | - Marcelo Teixeira
- Department of Academic Informatics (DAINF), Universidade Tecnologica Federal do Parana (UTFPR), Pato Branco 85503-390, PR, Brazil
| | - Jeferson T. Oliva
- Department of Academic Informatics (DAINF), Universidade Tecnologica Federal do Parana (UTFPR), Pato Branco 85503-390, PR, Brazil
| | - Giovani Bernardes
- Institute of Technological Sciences (ICT), Universidade Federal de Itajuba (UNIFEI), Itabira 35903-087, MG, Brazil
| | - Panos Liatsis
- Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
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18
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Tapp RJ, Owen CG, Barman SA, Strachan DP, Welikala RA, Foster PJ, Whincup PH, Rudnicka AR. Retinal microvascular associations with cardiometabolic risk factors differ by diabetes status: results from the UK Biobank. Diabetologia 2022; 65:1652-1663. [PMID: 35852586 PMCID: PMC9477904 DOI: 10.1007/s00125-022-05745-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 03/25/2022] [Indexed: 12/30/2022]
Abstract
AIMS/HYPOTHESIS The aim of the study was to examine the association of retinal vessel morphometry with BP, body composition and biochemistry, and to determine whether these associations differ by diabetes status. METHODS The UK Biobank ocular assessment included 68,550 participants aged 40-70 years who underwent non-mydriatic retinal photography, BP and body composition measurements, and haematological analysis. A fully automated image analysis program provided measurements of retinal vessel diameter and tortuosity. The associations between retinal vessel morphology and cardiometabolic risk factors by diabetes status were examined using multilevel linear regression, to provide absolute differences in vessel diameter and percentage differences in tortuosity (allowing for within-person clustering). RESULTS A total of 50,233 participants (a reduction from 68,550) were included in these analyses. Overall, those with diabetes had significantly more tortuous venules and wider arteriolar diameters compared with those without. Associations between venular tortuosity and cardiometabolic risk factors differed according to diabetes status (p interaction <0.01) for total fat mass index, HbA1c, C-reactive protein, white cell count and granulocyte count. For example, a unit rise in white cell count was associated with a 0.18% increase (95% CI 0.05, 0.32%) in venular tortuosity for those without diabetes and a 1.48% increase (95% CI 0.90, 2.07%) among those with diabetes. For arteriolar diameter, significant interactions were evident for systolic BP, diastolic BP, mean arterial pressure (MAP) and LDL-cholesterol. For example, a 10 mmHg rise in systolic BP was associated with a -0.92 μm difference (95% CI -0.96 to -0.88 μm) in arteriolar diameter for those without diabetes, and a -0.58 μm difference (95% CI -0.76 to -0.41 μm) among those with diabetes. No interactions were observed for arteriolar tortuosity or venular diameters. CONCLUSIONS/INTERPRETATION We provide clear evidence of the modifying effect of diabetes on cardiometabolic risk factor associations with retinal microvascular architecture. These observations suggest the occurrence of preclinical disease processes, and may be a sign of impaired autoregulation due to hyperglycaemia, which has been suggested to play a pivotal role in the development of diabetes-related microvascular complications. DATA AVAILABILITY The data supporting the results reported here are available through the UK Biobank ( https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access ).
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Affiliation(s)
- Robyn J Tapp
- Population Health Research Institute, St George's, University of London, London, UK.
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, UK.
| | - Christopher G Owen
- Population Health Research Institute, St George's, University of London, London, UK
| | - Sarah A Barman
- Faculty of Science, Engineering and Computing, Kingston University, Kingston upon Thames, Surrey, UK
| | - David P Strachan
- Population Health Research Institute, St George's, University of London, London, UK
| | - Roshan A Welikala
- Faculty of Science, Engineering and Computing, Kingston University, Kingston upon Thames, Surrey, UK
| | - Paul J Foster
- Integrative Epidemiology Research Group, UCL Institute of Ophthalmology, London, UK
- NIHR Biomedical Research Centre at Moorfields Eye Hospital, London, UK
| | - Peter H Whincup
- Population Health Research Institute, St George's, University of London, London, UK
| | - Alicja R Rudnicka
- Population Health Research Institute, St George's, University of London, London, UK.
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19
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Abtahi M, Le D, Lim JI, Yao X. MF-AV-Net: an open-source deep learning network with multimodal fusion options for artery-vein segmentation in OCT angiography. BIOMEDICAL OPTICS EXPRESS 2022; 13:4870-4888. [PMID: 36187235 PMCID: PMC9484445 DOI: 10.1364/boe.468483] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 08/12/2022] [Accepted: 08/12/2022] [Indexed: 06/16/2023]
Abstract
This study is to demonstrate the effect of multimodal fusion on the performance of deep learning artery-vein (AV) segmentation in optical coherence tomography (OCT) and OCT angiography (OCTA); and to explore OCT/OCTA characteristics used in the deep learning AV segmentation. We quantitatively evaluated multimodal architectures with early and late OCT-OCTA fusions, compared to the unimodal architectures with OCT-only and OCTA-only inputs. The OCTA-only architecture, early OCT-OCTA fusion architecture, and late OCT-OCTA fusion architecture yielded competitive performances. For the 6 mm×6 mm and 3 mm×3 mm datasets, the late fusion architecture achieved an overall accuracy of 96.02% and 94.00%, slightly better than the OCTA-only architecture which achieved an overall accuracy of 95.76% and 93.79%. 6 mm×6 mm OCTA images show AV information at pre-capillary level structure, while 3 mm×3 mm OCTA images reveal AV information at capillary level detail. In order to interpret the deep learning performance, saliency maps were produced to identify OCT/OCTA image characteristics for AV segmentation. Comparative OCT and OCTA saliency maps support the capillary-free zone as one of the possible features for AV segmentation in OCTA. The deep learning network MF-AV-Net used in this study is available on GitHub for open access.
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Affiliation(s)
- Mansour Abtahi
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
- These authors contributed equally to this work
| | - David Le
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
- These authors contributed equally to this work
| | - Jennifer I. Lim
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Xincheng Yao
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA
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20
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Hu J, Wang H, Wu G, Cao Z, Mou L, Zhao Y, Zhang J. Multi-scale Interactive Network with Artery/Vein Discriminator for Retinal Vessel Classification. IEEE J Biomed Health Inform 2022; 26:3896-3905. [PMID: 35394918 DOI: 10.1109/jbhi.2022.3165867] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Automatic classification of retinal arteries and veins plays an important role in assisting clinicians to diagnosis cardiovascular and eye-related diseases. However, due to the high degree of anatomical variation across the population, and the presence of inconsistent labels by the subjective judgment of annotators in available training data, most of existing methods generally suffer from blood vessel discontinuity and arteriovenous confusion, the artery/vein (A/V) classification task still faces great challenges. In this work, we propose a multi-scale interactive network with A/V discriminator for retinal artery and vein recognition, which can reduce the arteriovenous confusion and alleviate the disturbance of noisy label. A multi-scale interaction (MI) module is designed in encoder for realizing the cross-space multi-scale features interaction of fundus images, effectively integrate high-level and low-level context information. In particular, we design an ingenious A/V discriminator (AVD) that utilizes the independent and shared information between arteries and veins, and combine with topology loss, to further strengthen the learning ability of model to resolve the arteriovenous confusion. In addition, we adopt a sample re-weighting (SW) strategy to effectively alleviate the disturbance from data labeling errors. The proposed model is verified on three publicly available fundus image datasets (AV-DRIVE, HRF, LES-AV) and a private dataset. We achieve the accuracy of 97.47%, 96.91%, 97.79%, and 98.18% respectively on these four datasets. Extensive experimental results demonstrate that our method achieves competitive performance compared with state-of-the-art methods for A/V classification. To address the problem of training data scarcity, we publicly release 100 fundus images with A/V annotations to promote relevant research in the community.
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21
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TW-GAN: Topology and width aware GAN for retinal artery/vein classification. Med Image Anal 2022; 77:102340. [DOI: 10.1016/j.media.2021.102340] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 12/18/2021] [Accepted: 12/20/2021] [Indexed: 11/20/2022]
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22
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Hatamizadeh A, Hosseini H, Patel N, Choi J, Pole CC, Hoeferlin CM, Schwartz SD, Terzopoulos D. RAVIR: A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance Imaging. IEEE J Biomed Health Inform 2022; 26:3272-3283. [PMID: 35349464 DOI: 10.1109/jbhi.2022.3163352] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The retinal vasculature provides important clues in the diagnosis and monitoring of systemic diseases including hypertension and diabetes. The microvascular system is of primary involvement in such conditions, and the retina is the only anatomical site where the microvasculature can be directly observed. The objective assessment of retinal vessels has long been considered a surrogate biomarker for systemic vascular diseases, and with recent advancements in retinal imaging and computer vision technologies, this topic has become the subject of renewed attention. In this paper, we present a novel dataset, dubbed RAVIR, for the semantic segmentation of Retinal Arteries and Veins in Infrared Reflectance (IR) imaging. It enables the creation of deep learning-based models that distinguish extracted vessel type without extensive post-processing. We propose a novel deep learning-based methodology, denoted as SegRAVIR, for the semantic segmentation of retinal arteries and veins and the quantitative measurement of the widths of segmented vessels. Our extensive experiments validate the effectiveness of SegRAVIR and demonstrate its superior performance in comparison to state-of-the-art models. Additionally, we propose a knowledge distillation framework for the domain adaptation of RAVIR pretrained networks on color images. We demonstrate that our pretraining procedure yields new state-of-the-art benchmarks on the DRIVE, STARE, and CHASE\_DB1 datasets. Dataset link: https://ravirdataset.github.io/data.
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23
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Binh NT, Hien NM, Tin DT. Improving U-Net architecture and graph cuts optimization to classify arterioles and venules in retina fundus images. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The central retinal artery and its branches supply blood to the inner retina. Vascular manifestations in the retina indirectly reflect the vascular changes and damage in organs such as the heart, kidneys, and brain because of the similar vascular structure of these organs. The diabetic retinopathy and risk of stroke are caused by increased venular caliber. The degrees of these diseases depend on the changes of arterioles and venules. The ratio between the calibers of arterioles and venules (AVR) is various. AVR is considered as the useful diagnostic indicator of different associated health problems. However, the task is not easy because of the lack of information of the features being used to classify the retinal vessels as arterioles and venules. This paper proposed a method to classify the retinal vessels into the arterioles and venules based on improving U-Net architecture and graph cuts. The accuracy of the proposed method is about 97.6%. The results of the proposed method are better than the other methods in RITE dataset and AVRDB dataset.
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Affiliation(s)
- Nguyen Thanh Binh
- Department of Information Systems, Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Vietnam
- Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam
| | - Nguyen Mong Hien
- Department of Information Systems, Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Vietnam
- Tra Vinh University, Vietnam
| | - Dang Thanh Tin
- Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam
- Information Systems Engineering Laboratory, Faculty of Electrical and Electronics Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Vietnam
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Javeed A, Khan SU, Ali L, Ali S, Imrana Y, Rahman A. Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:9288452. [PMID: 35154361 PMCID: PMC8831075 DOI: 10.1155/2022/9288452] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 01/15/2022] [Indexed: 12/13/2022]
Abstract
One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. Traditional medical procedures (angiography) for the diagnosis of heart disease have higher cost as well as serious health concerns. Therefore, researchers have developed various automated diagnostic systems based on machine learning (ML) and data mining techniques. ML-based automated diagnostic systems provide an affordable, efficient, and reliable solutions for heart disease detection. Various ML, data mining methods, and data modalities have been utilized in the past. Many previous review papers have presented systematic reviews based on one type of data modality. This study, therefore, targets systematic review of automated diagnosis for heart disease prediction based on different types of modalities, i.e., clinical feature-based data modality, images, and ECG. Moreover, this paper critically evaluates the previous methods and presents the limitations in these methods. Finally, the article provides some future research directions in the domain of automated heart disease detection based on machine learning and multiple of data modalities.
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Affiliation(s)
- Ashir Javeed
- Aging Research Center, Karolinska Institutet, Sweden
| | - Shafqat Ullah Khan
- Department of Electrical Engineering, University of Science and Technology Bannu, Pakistan
| | - Liaqat Ali
- Department of Electronics, University of Buner, Buner, Pakistan
| | - Sardar Ali
- School of Engineering and Applied Sciences, Isra University Islamabad Campus, Pakistan
| | - Yakubu Imrana
- School of Engineering, University of Development Studies, Tamale, Ghana
- School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Atiqur Rahman
- Department of Computer Science, University of Science and Technology Bannu, Pakistan
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25
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Vinayaki VD, Kalaiselvi R. Multithreshold Image Segmentation Technique Using Remora Optimization Algorithm for Diabetic Retinopathy Detection from Fundus Images. Neural Process Lett 2022; 54:2363-2384. [PMID: 35095328 PMCID: PMC8784591 DOI: 10.1007/s11063-021-10734-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/24/2021] [Indexed: 12/21/2022]
Abstract
One of the most common complications of diabetes mellitus is diabetic retinopathy (DR), which produces lesions on the retina. A novel framework for DR detection and classification was proposed in this study. The proposed work includes four stages: pre-processing, segmentation, feature extraction, and classification. Initially, the image pre-processing is performed and after that, the Multi threshold-based Remora Optimization (MTRO) algorithm performs the vessel segmentation. The feature extraction and classification process are done by using a Region-based Convolution Neural Network (R-CNN) with Wild Geese Algorithm (WGA). Finally, the proposed R-CNN with WGA effectively classifies the different stages of DR including Non-DR, Proliferative DR, Severe, Moderate DR, Mild DR. The experimental images were collected from the DRIVE database, and the proposed framework exhibited superior DR detection performance. Compared to other existing methods like fully convolutional deep neural network (FCDNN), genetic-search feature selection (GSFS), Convolutional Neural Networks (CNN), and deep learning (DL) techniques, the proposed R-CNN with WGA provided 95.42% accuracy, 93.10% specificity, 93.20% sensitivity, and 98.28% F-score results.
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26
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Review of Machine Learning Applications Using Retinal Fundus Images. Diagnostics (Basel) 2022; 12:diagnostics12010134. [PMID: 35054301 PMCID: PMC8774893 DOI: 10.3390/diagnostics12010134] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 01/03/2022] [Accepted: 01/03/2022] [Indexed: 02/04/2023] Open
Abstract
Automating screening and diagnosis in the medical field saves time and reduces the chances of misdiagnosis while saving on labor and cost for physicians. With the feasibility and development of deep learning methods, machines are now able to interpret complex features in medical data, which leads to rapid advancements in automation. Such efforts have been made in ophthalmology to analyze retinal images and build frameworks based on analysis for the identification of retinopathy and the assessment of its severity. This paper reviews recent state-of-the-art works utilizing the color fundus image taken from one of the imaging modalities used in ophthalmology. Specifically, the deep learning methods of automated screening and diagnosis for diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are investigated. In addition, the machine learning techniques applied to the retinal vasculature extraction from the fundus image are covered. The challenges in developing these systems are also discussed.
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Abbas Q, Qureshi I, Ibrahim MEA. An Automatic Detection and Classification System of Five Stages for Hypertensive Retinopathy Using Semantic and Instance Segmentation in DenseNet Architecture. SENSORS (BASEL, SWITZERLAND) 2021; 21:6936. [PMID: 34696149 PMCID: PMC8538561 DOI: 10.3390/s21206936] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/13/2021] [Accepted: 10/15/2021] [Indexed: 12/23/2022]
Abstract
The stage and duration of hypertension are connected to the occurrence of Hypertensive Retinopathy (HR) of eye disease. Currently, a few computerized systems have been developed to recognize HR by using only two stages. It is difficult to define specialized features to recognize five grades of HR. In addition, deep features have been used in the past, but the classification accuracy is not up-to-the-mark. In this research, a new hypertensive retinopathy (HYPER-RETINO) framework is developed to grade the HR based on five grades. The HYPER-RETINO system is implemented based on pre-trained HR-related lesions. To develop this HYPER-RETINO system, several steps are implemented such as a preprocessing, the detection of HR-related lesions by semantic and instance-based segmentation and a DenseNet architecture to classify the stages of HR. Overall, the HYPER-RETINO system determined the local regions within input retinal fundus images to recognize five grades of HR. On average, a 10-fold cross-validation test obtained sensitivity (SE) of 90.5%, specificity (SP) of 91.5%, accuracy (ACC) of 92.6%, precision (PR) of 91.7%, Matthews correlation coefficient (MCC) of 61%, F1-score of 92% and area-under-the-curve (AUC) of 0.915 on 1400 HR images. Thus, the applicability of the HYPER-RETINO method to reliably diagnose stages of HR is verified by experimental findings.
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Affiliation(s)
- Qaisar Abbas
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia;
| | - Imran Qureshi
- Department of Computer Software Engineering, Military College of Signals, National University of Sciences and Technology (MCS-NUST), Islamabad 44000, Pakistan;
| | - Mostafa E. A. Ibrahim
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia;
- Department of Electrical Engineering, Benha Faculty of Engineering, Benha University, Qalubia, Benha 13518, Egypt
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Simultaneous segmentation and classification of the retinal arteries and veins from color fundus images. Artif Intell Med 2021; 118:102116. [PMID: 34412839 DOI: 10.1016/j.artmed.2021.102116] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 05/20/2021] [Accepted: 05/21/2021] [Indexed: 01/25/2023]
Abstract
BACKGROUND AND OBJECTIVES The study of the retinal vasculature represents a fundamental stage in the screening and diagnosis of many high-incidence diseases, both systemic and ophthalmic. A complete retinal vascular analysis requires the segmentation of the vascular tree along with the classification of the blood vessels into arteries and veins. Early automatic methods approach these complementary segmentation and classification tasks in two sequential stages. However, currently, these two tasks are approached as a joint semantic segmentation, because the classification results highly depend on the effectiveness of the vessel segmentation. In that regard, we propose a novel approach for the simultaneous segmentation and classification of the retinal arteries and veins from eye fundus images. METHODS We propose a novel method that, unlike previous approaches, and thanks to the proposal of a novel loss, decomposes the joint task into three segmentation problems targeting arteries, veins and the whole vascular tree. This configuration allows to handle vessel crossings intuitively and directly provides accurate segmentation masks of the different target vascular trees. RESULTS The provided ablation study on the public Retinal Images vessel Tree Extraction (RITE) dataset demonstrates that the proposed method provides a satisfactory performance, particularly in the segmentation of the different structures. Furthermore, the comparison with the state of the art shows that our method achieves highly competitive results in the artery/vein classification, while significantly improving the vascular segmentation. CONCLUSIONS The proposed multi-segmentation method allows to detect more vessels and better segment the different structures, while achieving a competitive classification performance. Also, in these terms, our approach outperforms the approaches of various reference works. Moreover, in contrast with previous approaches, the proposed method allows to directly detect the vessel crossings, as well as preserving the continuity of both arteries and veins at these complex locations.
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29
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Hu J, Wang H, Cao Z, Wu G, Jonas JB, Wang YX, Zhang J. Automatic Artery/Vein Classification Using a Vessel-Constraint Network for Multicenter Fundus Images. Front Cell Dev Biol 2021; 9:659941. [PMID: 34178986 PMCID: PMC8226261 DOI: 10.3389/fcell.2021.659941] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 04/19/2021] [Indexed: 11/24/2022] Open
Abstract
Retinal blood vessel morphological abnormalities are generally associated with cardiovascular, cerebrovascular, and systemic diseases, automatic artery/vein (A/V) classification is particularly important for medical image analysis and clinical decision making. However, the current method still has some limitations in A/V classification, especially the blood vessel edge and end error problems caused by the single scale and the blurred boundary of the A/V. To alleviate these problems, in this work, we propose a vessel-constraint network (VC-Net) that utilizes the information of vessel distribution and edge to enhance A/V classification, which is a high-precision A/V classification model based on data fusion. Particularly, the VC-Net introduces a vessel-constraint (VC) module that combines local and global vessel information to generate a weight map to constrain the A/V features, which suppresses the background-prone features and enhances the edge and end features of blood vessels. In addition, the VC-Net employs a multiscale feature (MSF) module to extract blood vessel information with different scales to improve the feature extraction capability and robustness of the model. And the VC-Net can get vessel segmentation results simultaneously. The proposed method is tested on publicly available fundus image datasets with different scales, namely, DRIVE, LES, and HRF, and validated on two newly created multicenter datasets: Tongren and Kailuan. We achieve a balance accuracy of 0.9554 and F1 scores of 0.7616 and 0.7971 for the arteries and veins, respectively, on the DRIVE dataset. The experimental results prove that the proposed model achieves competitive performance in A/V classification and vessel segmentation tasks compared with state-of-the-art methods. Finally, we test the Kailuan dataset with other trained fusion datasets, the results also show good robustness. To promote research in this area, the Tongren dataset and source code will be made publicly available. The dataset and code will be made available at https://github.com/huawang123/VC-Net.
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Affiliation(s)
- Jingfei Hu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Hefei Innovation Research Institute, Beihang University, Hefei, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China.,School of Biomedical Engineering, Anhui Medical University, Hefei, China
| | - Hua Wang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Hefei Innovation Research Institute, Beihang University, Hefei, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China.,School of Biomedical Engineering, Anhui Medical University, Hefei, China
| | - Zhaohui Cao
- Hefei Innovation Research Institute, Beihang University, Hefei, China
| | - Guang Wu
- Hefei Innovation Research Institute, Beihang University, Hefei, China
| | - Jost B Jonas
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing, China.,Department of Ophthalmology, Medical Faculty Mannheim of the Ruprecht-Karls-University Heidelberg, Mannheim, Germany
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing, China
| | - Jicong Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Hefei Innovation Research Institute, Beihang University, Hefei, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China.,School of Biomedical Engineering, Anhui Medical University, Hefei, China.,Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China
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30
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Alam MN, Le D, Yao X. Differential artery-vein analysis in quantitative retinal imaging: a review. Quant Imaging Med Surg 2021; 11:1102-1119. [PMID: 33654680 PMCID: PMC7829162 DOI: 10.21037/qims-20-557] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Accepted: 06/19/2020] [Indexed: 11/06/2022]
Abstract
Quantitative retinal imaging is essential for eye disease detection, staging classification, and treatment assessment. It is known that different eye diseases or severity stages can affect the artery and vein systems in different ways. Therefore, differential artery-vein (AV) analysis can improve the performance of quantitative retinal imaging. In this article, we provide a brief summary of technical rationales and clinical applications of differential AV analysis in fundus photography, optical coherence tomography (OCT), and OCT angiography (OCTA).
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Affiliation(s)
- Minhaj Nur Alam
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
| | - David Le
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Xincheng Yao
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL, USA
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31
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Relan D, Relan R. Unsupervised sorting of retinal vessels using locally consistent Gaussian mixtures. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 199:105894. [PMID: 33341476 DOI: 10.1016/j.cmpb.2020.105894] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 11/26/2020] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVES Retinal blood vessels classification into arterioles and venules is a major task for biomarker identification. Especially, clustering of retinal blood vessels is a challenging task due to factors affecting the images such as contrast variability, non-uniform illumination etc. Hence, a high performance automatic retinal vessel classification system is highly prized. In this paper, we propose a novel unsupervised methodology to classify retinal vessels extracted from fundus camera images into arterioles and venules. METHODS The proposed method utilises the homomorphic filtering (HF) to preprocess the input image for non-uniform illumination and denoising. In the next step, an unsupervised multiscale line operator segmentation technique is used to segment the retinal vasculature before extracting the discriminating features. Finally, the Locally Consistent Gaussian Mixture Model (LCGMM) is utilised for unsupervised sorting of retinal vessels. RESULTS The performance of the proposed unsupervised method was assessed using three publicly accessible databases: INSPIRE-AVR, VICAVR, and MESSIDOR. The proposed framework achieved 90.14%,90.3% and 93.8% classification rate in zone B for the three datasets respectively. CONCLUSIONS The proposed clustering framework provided high classification rate as compared to conventional Gaussian mixture model using Expectation-Maximisation (GMM-EM) approach, thus have a great capability to enhance computer assisted diagnosis and research in field of biomarker discovery.
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Affiliation(s)
- D Relan
- Department of Computer Science, BML Munjal University, Gurgaon, India.
| | - R Relan
- Department of Applied Mathematics and Computer Science (DTU Compute), Technical University of Denmark, Kongens Lyngby, Denmark; Siemens Energy, Gurgaon, India.
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Qummar S, Khan FG, Shah S, Khan A, Din A, Gao J. Deep Learning Techniques for Diabetic Retinopathy Detection. Curr Med Imaging 2021; 16:1201-1213. [DOI: 10.2174/1573405616666200213114026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 11/26/2019] [Accepted: 12/19/2019] [Indexed: 11/22/2022]
Abstract
Diabetes occurs due to the excess of glucose in the blood that may affect many organs
of the body. Elevated blood sugar in the body causes many problems including Diabetic Retinopathy
(DR). DR occurs due to the mutilation of the blood vessels in the retina. The manual detection
of DR by ophthalmologists is complicated and time-consuming. Therefore, automatic detection is
required, and recently different machine and deep learning techniques have been applied to detect
and classify DR. In this paper, we conducted a study of the various techniques available in the literature
for the identification/classification of DR, the strengths and weaknesses of available datasets
for each method, and provides the future directions. Moreover, we also discussed the different
steps of detection, that are: segmentation of blood vessels in a retina, detection of lesions, and other
abnormalities of DR.
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Affiliation(s)
- Sehrish Qummar
- Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan
| | - Fiaz Gul Khan
- Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan
| | - Sajid Shah
- Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan
| | - Ahmad Khan
- Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan
| | - Ahmad Din
- Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan
| | - Jinfeng Gao
- Department of Information Engineering, Huanghuai University, Henan, China
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33
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Topology-Aware Retinal Artery–Vein Classification via Deep Vascular Connectivity Prediction. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app11010320] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Retinal artery–vein (AV) classification is a prerequisite for quantitative analysis of retinal vessels, which provides a biomarker for neurologic, cardiac, and systemic diseases, as well as ocular diseases. Although convolutional neural networks have presented remarkable performance on AV classification, it often comes with a topological error, like an abrupt class flipping on the same vessel segment or a weakness for thin vessels due to their indistinct appearances. In this paper, we present a new method for AV classification where the underlying vessel topology is estimated to give consistent prediction along the actual vessel structure. We cast the vessel topology estimation as iterative vascular connectivity prediction, which is implemented as deep-learning-based pairwise classification. In consequence, a whole vessel graph is separated into sub-trees, and each of them is classified as an artery or vein in whole via a voting scheme. The effectiveness and efficiency of the proposed method is validated by conducting experiments on two retinal image datasets acquired using different imaging techniques called DRIVE and IOSTAR.
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Rodrigues EO, Conci A, Liatsis P. ELEMENT: Multi-Modal Retinal Vessel Segmentation Based on a Coupled Region Growing and Machine Learning Approach. IEEE J Biomed Health Inform 2020; 24:3507-3519. [PMID: 32750920 DOI: 10.1109/jbhi.2020.2999257] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Vascular structures in the retina contain important information for the detection and analysis of ocular diseases, including age-related macular degeneration, diabetic retinopathy and glaucoma. Commonly used modalities in diagnosis of these diseases are fundus photography, scanning laser ophthalmoscope (SLO) and fluorescein angiography (FA). Typically, retinal vessel segmentation is carried out either manually or interactively, which makes it time consuming and prone to human errors. In this research, we propose a new multi-modal framework for vessel segmentation called ELEMENT (vEsseL sEgmentation using Machine lEarning and coNnecTivity). This framework consists of feature extraction and pixel-based classification using region growing and machine learning. The proposed features capture complementary evidence based on grey level and vessel connectivity properties. The latter information is seamlessly propagated through the pixels at the classification phase. ELEMENT reduces inconsistencies and speeds up the segmentation throughput. We analyze and compare the performance of the proposed approach against state-of-the-art vessel segmentation algorithms in three major groups of experiments, for each of the ocular modalities. Our method produced higher overall performance, with an overall accuracy of 97.40%, compared to 25 of the 26 state-of-the-art approaches, including six works based on deep learning, evaluated on the widely known DRIVE fundus image dataset. In the case of the STARE, CHASE-DB, VAMPIRE FA, IOSTAR SLO and RC-SLO datasets, the proposed framework outperformed all of the state-of-the-art methods with accuracies of 98.27%, 97.78%, 98.34%, 98.04% and 98.35%, respectively.
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Rudnicka AR, Owen CG, Welikala RA, Barman SA, Whincup PH, Strachan DP, Chan MP, Khawaja AP, Broadway DC, Luben R, Hayat SA, Khaw KT, Foster PJ. Retinal Vasculometry Associations With Glaucoma: Findings From the European Prospective Investigation of Cancer-Norfolk Eye Study. Am J Ophthalmol 2020; 220:140-151. [PMID: 32717267 PMCID: PMC7706353 DOI: 10.1016/j.ajo.2020.07.027] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Revised: 07/16/2020] [Accepted: 07/17/2020] [Indexed: 12/13/2022]
Abstract
Purpose To examine retinal vasculometry associations with different glaucomas in older British people. Design Cross-sectional study. Methods A total of 8,623 European Prospective Investigation into Cancer-Norfolk Eye study participants were examined, who underwent retinal imaging, ocular biometry assessment, and clinical ascertainment of ocular hypertensive or glaucoma status (including glaucoma suspect [GS], high-tension open-angle glaucoma [HTG], and normal-tension glaucoma [NTG]). Automated measures of arteriolar and venular tortuosity, area, and width from retinal images were obtained. MainOutcomeMeasures: Associations between glaucoma and retinal vasculometry outcomes were analyzed using multilevel linear regression, adjusted for age, sex, height, axial length, intraocular and systemic blood pressure, and within-person clustering, to provide absolute differences in width and area, and percentage differences in vessel tortuosity. Presence or absence of within-person-between-eye differences in retinal vasculometry by diagnoses were examined. Results A total of 565,593 vessel segments from 5,947 participants (mean age 67.6 years, SD 7.6 years, 57% women) were included; numbers with HTG, NTG, and GS in at least 1 eye were 87, 82, and 439, respectively. Thinner arterioles (−3.2 μm; 95% confidence interval [CI] −4.4 μm, −1.9 μm) and venules (−2.7 μm; 95% CI −4.9 μm, −0.5 μm) were associated with HTG. Reduced venular area was associated with HTG (−0.2 mm2; 95% CI −0.3 mm2, −0.1 mm2) and NTG (−0.2 mm2; 95% CI −0.3 mm2, −0.0 mm2). Less tortuous retinal arterioles and venules were associated with all glaucomas, but only significantly for GS (−3.9%; 95% CI −7.7%, −0.1% and −4.8%; 95% CI −7.4%, −2.1%, respectively). There was no evidence of within-person-between-eye differences in retinal vasculometry associations by diagnoses. Conclusions Retinal vessel width associations with glaucoma and novel associations with vessel area and tortuosity, together with no evidence of within-person-between-eye differences in retinal vasculometry, suggest a vascular cause of glaucoma. Retinal vessel measurements, including (as a first report) vessel tortuosity and area, were associated with high-tension glaucoma and other glaucoma-related outcomes. Novel analyses showing that within-person-between-eye glaucoma diagnoses, intraocular pressure, and retinal vasculometry were uncorrelated provides further evidence that systemic microvascular changes may cause glaucoma.
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36
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Mookiah MRK, Hogg S, MacGillivray TJ, Prathiba V, Pradeepa R, Mohan V, Anjana RM, Doney AS, Palmer CNA, Trucco E. A review of machine learning methods for retinal blood vessel segmentation and artery/vein classification. Med Image Anal 2020; 68:101905. [PMID: 33385700 DOI: 10.1016/j.media.2020.101905] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 11/10/2020] [Accepted: 11/11/2020] [Indexed: 12/20/2022]
Abstract
The eye affords a unique opportunity to inspect a rich part of the human microvasculature non-invasively via retinal imaging. Retinal blood vessel segmentation and classification are prime steps for the diagnosis and risk assessment of microvascular and systemic diseases. A high volume of techniques based on deep learning have been published in recent years. In this context, we review 158 papers published between 2012 and 2020, focussing on methods based on machine and deep learning (DL) for automatic vessel segmentation and classification for fundus camera images. We divide the methods into various classes by task (segmentation or artery-vein classification), technique (supervised or unsupervised, deep and non-deep learning, hand-crafted methods) and more specific algorithms (e.g. multiscale, morphology). We discuss advantages and limitations, and include tables summarising results at-a-glance. Finally, we attempt to assess the quantitative merit of DL methods in terms of accuracy improvement compared to other methods. The results allow us to offer our views on the outlook for vessel segmentation and classification for fundus camera images.
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Affiliation(s)
| | - Stephen Hogg
- VAMPIRE project, Computing (SSEN), University of Dundee, Dundee DD1 4HN, UK
| | - Tom J MacGillivray
- VAMPIRE project, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Vijayaraghavan Prathiba
- Madras Diabetes Research Foundation and Dr. Mohan's Diabetes Specialities Centre, Gopalapuram, Chennai 600086, India
| | - Rajendra Pradeepa
- Madras Diabetes Research Foundation and Dr. Mohan's Diabetes Specialities Centre, Gopalapuram, Chennai 600086, India
| | - Viswanathan Mohan
- Madras Diabetes Research Foundation and Dr. Mohan's Diabetes Specialities Centre, Gopalapuram, Chennai 600086, India
| | - Ranjit Mohan Anjana
- Madras Diabetes Research Foundation and Dr. Mohan's Diabetes Specialities Centre, Gopalapuram, Chennai 600086, India
| | - Alexander S Doney
- Division of Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, UK
| | - Colin N A Palmer
- Division of Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, UK
| | - Emanuele Trucco
- VAMPIRE project, Computing (SSEN), University of Dundee, Dundee DD1 4HN, UK
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A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre. Nat Biomed Eng 2020; 5:498-508. [PMID: 33046867 DOI: 10.1038/s41551-020-00626-4] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Accepted: 09/08/2020] [Indexed: 12/16/2022]
Abstract
Retinal blood vessels provide information on the risk of cardiovascular disease (CVD). Here, we report the development and validation of deep-learning models for the automated measurement of retinal-vessel calibre in retinal photographs, using diverse multiethnic multicountry datasets that comprise more than 70,000 images. Retinal-vessel calibre measured by the models and by expert human graders showed high agreement, with overall intraclass correlation coefficients of between 0.82 and 0.95. The models performed comparably to or better than expert graders in associations between measurements of retinal-vessel calibre and CVD risk factors, including blood pressure, body-mass index, total cholesterol and glycated-haemoglobin levels. In retrospectively measured prospective datasets from a population-based study, baseline measurements performed by the deep-learning system were associated with incident CVD. Our findings motivate the development of clinically applicable explainable end-to-end deep-learning systems for the prediction of CVD on the basis of the features of retinal vessels in retinal photographs.
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Kang H, Gao Y, Guo S, Xu X, Li T, Wang K. AVNet: A retinal artery/vein classification network with category-attention weighted fusion. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 195:105629. [PMID: 32634648 DOI: 10.1016/j.cmpb.2020.105629] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 06/21/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Automatic artery/vein (A/V) classification in retinal images is of great importance in detecting vascular abnormalities, which may provide biomarkers for early diagnosis of many systemic diseases. It is intuitive to apply popular deep semantic segmentation network for A/V classification. However, the model is required to provide powerful representation ability since vessel is much more complex than general objects. Moreover, deep network may lead to inconsistent classification results for the same vessel due to the lack of structured optimization objective. METHODS In this paper, we propose a novel segmentation network named AVNet, which effectively enhances the classification ability of the model by integrating category-attention weighted fusion (CWF) module, significantly improving the pixel-level A/V classification results. Then, a graph based vascular structure reconstruction (VSR) algorithm is employed to reduce the segment-wise inconsistency, verifying the effect of the graph model on noisy vessel segmentation results. RESULTS The proposed method has been verified on three datasets, i.e. DRIVE, LES-AV and WIDE. AVNet achieves pixel-level accuracies of 90.62%, 90.34%, and 93.16%, respectively, and VSR further improves the performance by 0.19%, 1.85% and 0.64%, achieving the state-of-the-art results on these three datasets. CONCLUSION The proposed method achieves competitive performance in A/V classification task.
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Affiliation(s)
- Hong Kang
- College of Computer Science, Nankai University, Tianjin, China; Beijing Shanggong Medical Technology Co. Ltd., China
| | - Yingqi Gao
- College of Computer Science, Nankai University, Tianjin, China
| | - Song Guo
- College of Computer Science, Nankai University, Tianjin, China
| | - Xia Xu
- College of Computer Science, Nankai University, Tianjin, China
| | - Tao Li
- College of Computer Science, Nankai University, Tianjin, China; State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Science, Beijing 100190, China
| | - Kai Wang
- College of Computer Science, Nankai University, Tianjin, China; Key Laboratory for Medical Data Analysis and Statistical Research of Tianjin, China.
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Tapp RJ, Owen CG, Barman SA, Welikala RA, Foster PJ, Whincup PH, Strachan DP, Rudnicka AR. Retinal Vascular Tortuosity and Diameter Associations with Adiposity and Components of Body Composition. Obesity (Silver Spring) 2020; 28:1750-1760. [PMID: 32725961 PMCID: PMC7116641 DOI: 10.1002/oby.22885] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 03/18/2020] [Accepted: 05/06/2020] [Indexed: 01/07/2023]
Abstract
OBJECTIVE The aim of this study was to assess whether adiposity or body composition relates to microvascular characteristics of the retina, indicative of cardiometabolic function. METHODS A fully automated QUARTZ software processed retinal images from 68,550 UK Biobank participants (aged 40-69 years). Differences in retinal vessel diameter and tortuosity with body composition measures from the Tanita analyzer were obtained by using multilevel regression analyses adjusted for age, sex, ethnicity, clinic, smoking, and Townsend deprivation index. RESULTS Venular tortuosity and diameter increased by approximately 2% (P < 10-300 ) and 0.6 μm (P < 10-6 ), respectively, per SD increase in BMI, waist circumference index, waist-hip ratio, total body fat mass index, and fat-free mass index (FFMI). Venular associations with adiposity persisted after adjustment for FFMI, whereas associations with FFMI were weakened by FMI adjustment. Arteriolar diameter (not tortuosity) narrowing with FFMI was independent of adiposity (-0.6 μm; -0.7 to -0.4 μm per SD increment of FFMI), while adiposity associations with arteriolar diameter were largely nonsignificant after adjustment for FFMI. CONCLUSIONS This demonstrates, on an unprecedented scale, that venular tortuosity and diameter are more strongly associated with adiposity, whereas arteriolar diameter relates more strongly to fat-free mass. Different attributes of the retinal microvasculature may reflect distinct roles of body composition and fatness on the cardiometabolic system.
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Affiliation(s)
- Robyn J Tapp
- Population Health Research Institute, St George's, University of London, London, UK
- Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Christopher G Owen
- Population Health Research Institute, St George's, University of London, London, UK
| | - Sarah A Barman
- Faculty of Science, Engineering and Computing, Kingston University, Surrey, UK
| | - Roshan A Welikala
- Faculty of Science, Engineering and Computing, Kingston University, Surrey, UK
| | - Paul J Foster
- Integrative Epidemiology Research Group, UCL Institute of Ophthalmology, London, UK
- NIHR Biomedical Research Centre at Moorfields Eye Hospital, London, UK
| | - Peter H Whincup
- Population Health Research Institute, St George's, University of London, London, UK
| | - David P Strachan
- Population Health Research Institute, St George's, University of London, London, UK
| | - Alicja R Rudnicka
- Population Health Research Institute, St George's, University of London, London, UK
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Huang F, Tan T, Dashtbozorg B, Zhou Y, Romeny BMTH. From Local to Global: A Graph Framework for Retinal Artery/Vein Classification. IEEE Trans Nanobioscience 2020; 19:589-597. [PMID: 32746331 DOI: 10.1109/tnb.2020.3004481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Fundus photography has been widely used for inspecting eye disorders by ophthalmologists or computer algorithms. Biomarkers related to retinal vessels plays an essential role to detect early diabetes. To quantify vascular biomarkers or the corresponding changes, an accurate artery and vein classification is necessary. In this work, we propose a new framework to boost local vessel classification with a global vascular network model using graph convolution. We compare our proposed method with two traditional state-of-the-art methods on a testing dataset of 750 images from the Maastricht Study. After incorporating global information, our model achieves the best accuracy of 86.45% compared to 85.5% from convolutional neural networks (CNN) and 82.9% from handcrafted pixel feature classification (HPFC). Our model also obtains the best area under receiver operating characteristic curve (AUC) of 0.95, compared to 0.93 from CNN and 0.90 from HPFC. The new classification framework has the advantage of easy deployment on top of local classification features. It corrects the local classification error by minimizing global classification error and it brings free additional classification performance.
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Yang J, Dong X, Hu Y, Peng Q, Tao G, Ou Y, Cai H, Yang X. Fully Automatic Arteriovenous Segmentation in Retinal Images via Topology-Aware Generative Adversarial Networks. Interdiscip Sci 2020; 12:323-334. [DOI: 10.1007/s12539-020-00385-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 06/16/2020] [Accepted: 07/08/2020] [Indexed: 10/23/2022]
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Shan H, Jia X, Yan P, Li Y, Paganetti H, Wang G. Synergizing medical imaging and radiotherapy with deep learning. MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2020. [DOI: 10.1088/2632-2153/ab869f] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Robertson G, Fleming A, Williams MC, Trucco E, Quinn N, Hogg R, McKay GJ, Kee F, Young I, Pellegrini E, Newby DE, van Beek EJR, Peto T, Dhillon B, van Hemert J, MacGillivray TJ. Association between hypertension and retinal vascular features in ultra-widefield fundus imaging. Open Heart 2020; 7:e001124. [PMID: 32076560 PMCID: PMC6999694 DOI: 10.1136/openhrt-2019-001124] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 11/27/2019] [Accepted: 12/17/2019] [Indexed: 01/14/2023] Open
Abstract
Objective Changes to the retinal vasculature are known to be associated with hypertension independently of traditional risk factors. We investigated whether measurements of retinal vascular calibre from ultra-widefield fundus imaging were associated with hypertensive status. Methods We retrospectively collected and semiautomatically measured ultra-widefield retinal fundus images from a subset of participants enrolled in an ongoing population study of ageing, categorised as normotensive or hypertensive according to thresholds on systolic/diastolic blood pressure (140/90 mm Hg) measured in a clinical setting. Vascular calibre in the peripheral retina was measured to calculate the nasal–annular arteriole:venule ratio (NA-AVR), a novel combined parameter. Results Left and right eyes were analysed from 440 participants (aged 50–59 years, mean age of 54.6±2.9 years, 247, 56.1% women), including 151 (34.3%) categorised as hypertensive. Arterioles were thinner and the NA-AVR was smaller in people with hypertension. The area under the receiver operating characteristic curve of NA-AVR for hypertensive status was 0.73 (95% CI 0.68 to 0.78) using measurements from left eyes, while for right eyes, it was 0.64 (95% CI 0.59 to 0.70), representing evidence of a statistically significant difference between the eyes (p=0.020). Conclusions Semiautomated measurements of NA-AVR in ultra-widefield fundus imaging were associated with hypertension. With further development, this may help screen people attending routine eye health check-ups for high blood pressure. These individuals may then follow a care pathway for suspected hypertension. Our results showed differences between left and right eyes, highlighting the importance of investigating both eyes of a patient.
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Affiliation(s)
| | | | | | - Emanuele Trucco
- The VAMPIRE Project, Computer Vision and Image Processing Group, School of Science and Engineering, University of Dundee, Dundee, Dundee, UK
| | - Nicola Quinn
- Centre for Public Health, Queen's University Belfast, Belfast, Belfast, UK
| | - Ruth Hogg
- Centre for Public Health, Queen's University Belfast, Belfast, Belfast, UK
| | - Gareth J McKay
- Centre for Public Health, Queen's University Belfast, Belfast, Belfast, UK
| | - Frank Kee
- Centre for Public Health, Queen's University Belfast, Belfast, Belfast, UK
| | - Ian Young
- Centre for Public Health, Queen's University Belfast, Belfast, Belfast, UK
| | | | - David E Newby
- Centre for Cardiovascular Sciences, University of Edinburgh, Edinburgh, Lothian, UK
| | - Edwin J R van Beek
- Centre for Cardiovascular Sciences, University of Edinburgh, Edinburgh, Lothian, UK.,Edinburgh Imaging Facility QMRI, University of Edinburgh, Edinburgh, UK
| | - Tunde Peto
- Centre for Public Health, Queen's University Belfast, Belfast, Belfast, UK
| | - Baljean Dhillon
- The VAMPIRE Project, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | | | - Thomas J MacGillivray
- Edinburgh Imaging Facility QMRI, University of Edinburgh, Edinburgh, UK.,The VAMPIRE Project, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
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Haddad SMH, Scott CJM, Ozzoude M, Holmes MF, Arnott SR, Nanayakkara ND, Ramirez J, Black SE, Dowlatshahi D, Strother SC, Swartz RH, Symons S, Montero-Odasso M, Bartha R. Comparison of quality control methods for automated diffusion tensor imaging analysis pipelines. PLoS One 2019; 14:e0226715. [PMID: 31860686 PMCID: PMC6924651 DOI: 10.1371/journal.pone.0226715] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 12/02/2019] [Indexed: 12/29/2022] Open
Abstract
The processing of brain diffusion tensor imaging (DTI) data for large cohort studies requires fully automatic pipelines to perform quality control (QC) and artifact/outlier removal procedures on the raw DTI data prior to calculation of diffusion parameters. In this study, three automatic DTI processing pipelines, each complying with the general ENIGMA framework, were designed by uniquely combining multiple image processing software tools. Different QC procedures based on the RESTORE algorithm, the DTIPrep protocol, and a combination of both methods were compared using simulated ground truth and artifact containing DTI datasets modeling eddy current induced distortions, various levels of motion artifacts, and thermal noise. Variability was also examined in 20 DTI datasets acquired in subjects with vascular cognitive impairment (VCI) from the multi-site Ontario Neurodegenerative Disease Research Initiative (ONDRI). The mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated in global brain grey matter (GM) and white matter (WM) regions. For the simulated DTI datasets, the measure used to evaluate the performance of the pipelines was the normalized difference between the mean DTI metrics measured in GM and WM regions and the corresponding ground truth DTI value. The performance of the proposed pipelines was very similar, particularly in FA measurements. However, the pipeline based on the RESTORE algorithm was the most accurate when analyzing the artifact containing DTI datasets. The pipeline that combined the DTIPrep protocol and the RESTORE algorithm produced the lowest standard deviation in FA measurements in normal appearing WM across subjects. We concluded that this pipeline was the most robust and is preferred for automated analysis of multisite brain DTI data.
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Affiliation(s)
- Seyyed M. H. Haddad
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
| | - Christopher J. M. Scott
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Miracle Ozzoude
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Melissa F. Holmes
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Stephen R. Arnott
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario, Canada
| | - Nuwan D. Nanayakkara
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
| | - Joel Ramirez
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Sandra E. Black
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
- Department of Medicine, Division of Neurology, Sunnybrook Health Sciences Centre, and University of Toronto, Toronto, Ontario, Canada
| | | | - Stephen C. Strother
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Richard H. Swartz
- Department of Medicine, Division of Neurology, Sunnybrook Health Sciences Centre, and University of Toronto, Toronto, Ontario, Canada
- Sunnybrook Health Sciences Centre, University of Toronto, Stroke Research Program, Toronto, Ontario, Canada
| | - Sean Symons
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Manuel Montero-Odasso
- Department of Medicine, Division of Geriatric Medicine, Parkwood Hospital, University of Western Ontario, London, Ontario, Canada
| | | | - Robert Bartha
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada
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Yin XX, Irshad S, Zhang Y. Artery/vein classification of retinal vessels using classifiers fusion. Health Inf Sci Syst 2019; 7:26. [PMID: 31749960 DOI: 10.1007/s13755-019-0090-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 10/28/2019] [Indexed: 11/28/2022] Open
Abstract
The morphological changes in retinal blood vessels indicate cardiovascular diseases and consequently those diseases lead to ocular complications such as Hypertensive Retinopathy. One of the significant clinical findings related to this ocular abnormality is alteration of width of vessel. The classification of retinal vessels into arteries and veins in eye fundus images is a relevant task for the automatic assessment of vascular changes. This paper presents an important approach to solve this problem by means of feature ranking strategies and multiple classifiers decision-combination scheme that is specifically adapted for artery/vein classification. For this, three databases are used with a local dataset of 44 images and two publically available databases, INSPIRE-AVR containing 40 images and VICAVR containing 58 images. The local database also contains images with pathologically diseased structures. The performance of the proposed system is assessed by comparing the experimental results with the gold standard estimations as well as with the results of previous methodologies, achieving promising classification performance, with an over all accuracy of 90.45%, 93.90% and 87.82%, in retinal blood vessel separation for Local, INSPIRE-AVR and VICAVR dataset, respectively.
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Affiliation(s)
- Xiao-Xia Yin
- 1Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China
| | - Samra Irshad
- 2Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia
| | - Yanchun Zhang
- 2Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia
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Multiloss Function Based Deep Convolutional Neural Network for Segmentation of Retinal Vasculature into Arterioles and Venules. BIOMED RESEARCH INTERNATIONAL 2019; 2019:4747230. [PMID: 31111055 PMCID: PMC6487175 DOI: 10.1155/2019/4747230] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 02/20/2019] [Accepted: 03/20/2019] [Indexed: 02/02/2023]
Abstract
The arterioles and venules (AV) classification of retinal vasculature is considered as the first step in the development of an automated system for analysing the vasculature biomarker association with disease prognosis. Most of the existing AV classification methods depend on the accurate segmentation of retinal blood vessels. Moreover, the unavailability of large-scale annotated data is a major hindrance in the application of deep learning techniques for AV classification. This paper presents an encoder-decoder based fully convolutional neural network for classification of retinal vasculature into arterioles and venules, without requiring the preliminary step of vessel segmentation. An optimized multiloss function is used to learn the pixel-wise and segment-wise retinal vessel labels. The proposed method is trained and evaluated on DRIVE, AVRDB, and a newly created AV classification dataset; and it attains 96%, 98%, and 97% accuracy, respectively. The new AV classification dataset is comprised of 700 annotated retinal images, which will offer the researchers a benchmark to compare their AV classification results.
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Xu X, Yannuzzi NA, Fernández-Avellaneda P, Echegaray JJ, Tran KD, Russell JF, Patel NA, Hussain RM, Sarraf D, Freund KB. Differentiating Veins From Arteries on Optical Coherence Tomography Angiography by Identifying Deep Capillary Plexus Vortices. Am J Ophthalmol 2019; 207:363-372. [PMID: 31226248 DOI: 10.1016/j.ajo.2019.06.009] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2019] [Revised: 06/07/2019] [Accepted: 06/08/2019] [Indexed: 01/26/2023]
Abstract
PURPOSE To introduce a simple method for differentiating retinal veins from arteries on optical coherence tomography angiography (OCTA). DESIGN Cross-sectional pilot study. METHODS Four default en face slabs including color depth encoded, grayscale full-thickness retina, superficial plexus, and deep capillary plexus (DCP) from nine 3×3-mm and nine 6×6-mm OCTA scans were exported and aligned. Nine ophthalmologists with minimum OCTA experience from 2 eye institutions were instructed to classify labeled vessels as arteries or veins in 3 stages. Classification was performed based on graders' own assessment at stage 1. Graders were taught that a capillary-free zone was an anatomic feature of arteries at stage 2 and were trained to identify veins originating from vortices within the DCP at stage 3. Grading accuracy was analyzed and correlated with grading time and graders' years in practice. RESULTS Overall grading accuracy in stages 1, 2, and 3 was (50.4% ± 17.0%), (75.4% ± 6.0%), and (94.7% ± 2.6%), respectively. Grading accuracy for 3×3-mm scans in stages 1, 2, and 3 was (49.9% ± 16.3%), (79.2% ± 9.6%), and (96.9% ± 3.1%), respectively. Accuracy for 6×6-mm scans in stages 1, 2, and 3 was (51.4% ± 20.8%), (72.3% ± 7.9%), and (93.2% ± 3.3%), respectively. Grading performance improved significantly at each stage (all P < .001). No significant correlation was found between accuracy and time spent grading or between accuracy and years in practice (r = -0.164 to 0.617, all P ≥ .077). CONCLUSIONS We describe a simple method for accurately distinguishing retinal arteries from veins on OCTA, which incorporates the use of vortices in the DCP to identify venous origin.
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Affiliation(s)
- Xiaoyu Xu
- Vitreous Retina Macula Consultants of New York, New York, New York, USA; LuEsther T. Mertz Retinal Research Center, Manhattan Eye, Ear, and Throat Hospital, New York, New York, USA; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Nicolas A Yannuzzi
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Pedro Fernández-Avellaneda
- Vitreous Retina Macula Consultants of New York, New York, New York, USA; LuEsther T. Mertz Retinal Research Center, Manhattan Eye, Ear, and Throat Hospital, New York, New York, USA; Department of Ophthalmology, Basurto University Hospital, Bilbao, Spain
| | - Jose J Echegaray
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Kimberly D Tran
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Jonathan F Russell
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Nimesh A Patel
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Rehan M Hussain
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - David Sarraf
- Stein Eye Institute, University of California, Los Angeles, Los Angeles, California, USA
| | - K Bailey Freund
- Vitreous Retina Macula Consultants of New York, New York, New York, USA; LuEsther T. Mertz Retinal Research Center, Manhattan Eye, Ear, and Throat Hospital, New York, New York, USA; Department of Ophthalmology, New York University of Medicine; Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, New York, USA.
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Tapp RJ, Owen CG, Barman SA, Welikala RA, Foster PJ, Whincup PH, Strachan DP, Rudnicka AR. Associations of Retinal Microvascular Diameters and Tortuosity With Blood Pressure and Arterial Stiffness: United Kingdom Biobank. Hypertension 2019; 74:1383-1390. [PMID: 31661987 PMCID: PMC7069386 DOI: 10.1161/hypertensionaha.119.13752] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Supplemental Digital Content is available in the text. To examine the baseline associations of retinal vessel morphometry with blood pressure (BP) and arterial stiffness in United Kingdom Biobank. The United Kingdom Biobank included 68 550 participants aged 40 to 69 years who underwent nonmydriatic retinal imaging, BP, and arterial stiffness index assessment. A fully automated image analysis program (QUARTZ [Quantitative Analysis of Retinal Vessel Topology and Size]) provided measures of retinal vessel diameter and tortuosity. The associations between retinal vessel morphology and cardiovascular disease risk factors/outcomes were examined using multilevel linear regression to provide absolute differences in vessel diameter and percentage differences in tortuosity (allowing within person clustering), adjusted for age, sex, ethnicity, clinic, body mass index, smoking, and deprivation index. Greater arteriolar tortuosity was associated with higher systolic BP (relative increase, 1.2%; 95% CI, 0.9; 1.4% per 10 mmHg), higher mean arterial pressure, 1.3%; 0.9, 1.7% per 10 mmHg, and higher pulse pressure (PP, 1.8%; 1.4; 2.2% per 10 mmHg). Narrower arterioles were associated with higher systolic BP (−0.9 µm; −0.94, −0.87 µm per 10 mmHg), mean arterial pressure (−1.5 µm; −1.5, −1.5 µm per 10 mmHg), PP (−0.7 µm; −0.8, −0.7 µm per 10 mmHg), and arterial stiffness index (−0.12 µm; −0.14, −0.09 µm per ms/m2). Associations were in the same direction but marginally weaker for venular tortuosity and diameter. This study assessing the retinal microvasculature at scale has shown clear associations between retinal vessel morphometry, BP, and arterial stiffness index. These observations further our understanding of the preclinical disease processes and interplay between microvascular and macrovascular disease.
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Affiliation(s)
- Robyn J Tapp
- From the Population Health Research Institute, St George's University of London, United Kingdom (R.J.T., C.G.O., P.H.W., D.P.S., A.R.R.).,Melbourne School of Population and Global Health, University of Melbourne, Australia (R.J.T.)
| | - Christopher G Owen
- From the Population Health Research Institute, St George's University of London, United Kingdom (R.J.T., C.G.O., P.H.W., D.P.S., A.R.R.)
| | - Sarah A Barman
- Faculty of Science, Engineering and Computing, Kingston University, Surrey, United Kingdom (S.A.B., R.A.W.)
| | - Roshan A Welikala
- Faculty of Science, Engineering and Computing, Kingston University, Surrey, United Kingdom (S.A.B., R.A.W.)
| | - Paul J Foster
- Integrative Epidemiology Research Group, UCL Institute of Ophthalmology, United Kingdom (P.J.F.).,NIHR Biomedical Research Centre at Moorfields Eye Hospital, United Kingdom (P.J.F.)
| | - Peter H Whincup
- From the Population Health Research Institute, St George's University of London, United Kingdom (R.J.T., C.G.O., P.H.W., D.P.S., A.R.R.)
| | - David P Strachan
- From the Population Health Research Institute, St George's University of London, United Kingdom (R.J.T., C.G.O., P.H.W., D.P.S., A.R.R.)
| | - Alicja R Rudnicka
- From the Population Health Research Institute, St George's University of London, United Kingdom (R.J.T., C.G.O., P.H.W., D.P.S., A.R.R.)
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Hemelings R, Elen B, Stalmans I, Van Keer K, De Boever P, Blaschko MB. Artery-vein segmentation in fundus images using a fully convolutional network. Comput Med Imaging Graph 2019; 76:101636. [PMID: 31288217 DOI: 10.1016/j.compmedimag.2019.05.004] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 05/18/2019] [Accepted: 05/24/2019] [Indexed: 10/26/2022]
Abstract
Epidemiological studies demonstrate that dimensions of retinal vessels change with ocular diseases, coronary heart disease and stroke. Different metrics have been described to quantify these changes in fundus images, with arteriolar and venular calibers among the most widely used. The analysis often includes a manual procedure during which a trained grader differentiates between arterioles and venules. This step can be time-consuming and can introduce variability, especially when large volumes of images need to be analyzed. In light of the recent successes of fully convolutional networks (FCNs) applied to biomedical image segmentation, we assess its potential in the context of retinal artery-vein (A/V) discrimination. To the best of our knowledge, a deep learning (DL) architecture for simultaneous vessel extraction and A/V discrimination has not been previously employed. With the aim of improving the automation of vessel analysis, a novel application of the U-Net semantic segmentation architecture (based on FCNs) on the discrimination of arteries and veins in fundus images is presented. By utilizing DL, results are obtained that exceed accuracies reported in the literature. Our model was trained and tested on the public DRIVE and HRF datasets. For DRIVE, measuring performance on vessels wider than two pixels, the FCN achieved accuracies of 94.42% and 94.11% on arteries and veins, respectively. This represents a decrease in error of 25% over the previous state of the art reported by Xu et al. (2017). Additionally, we introduce the HRF A/V ground truth, on which our model achieves 96.98% accuracy on all discovered centerline pixels. HRF A/V ground truth validated by an ophthalmologist, predicted A/V annotations and evaluation code are available at https://github.com/rubenhx/av-segmentation.
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Affiliation(s)
- Ruben Hemelings
- Research Group Ophthalmology, KU Leuven, Kapucijnenvoer 33, 3000 Leuven, Belgium; ESAT-PSI, KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium; VITO NV, Boeretang 200, 2400 Mol, Belgium
| | - Bart Elen
- VITO NV, Boeretang 200, 2400 Mol, Belgium
| | - Ingeborg Stalmans
- Research Group Ophthalmology, KU Leuven, Kapucijnenvoer 33, 3000 Leuven, Belgium
| | - Karel Van Keer
- Research Group Ophthalmology, KU Leuven, Kapucijnenvoer 33, 3000 Leuven, Belgium
| | - Patrick De Boever
- Hasselt University, Agoralaan building D, 3590 Diepenbeek, Belgium; VITO NV, Boeretang 200, 2400 Mol, Belgium.
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Lu D, Heisler M, Lee S, Ding GW, Navajas E, Sarunic MV, Beg MF. Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network. Med Image Anal 2019; 54:100-110. [DOI: 10.1016/j.media.2019.02.011] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 02/15/2019] [Accepted: 02/15/2019] [Indexed: 11/28/2022]
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