Validation Study on Retinal Vessel Caliber Measurement Technique

Huang, Fan; Dashtbozorg, Behdad; Zhang, Jiong; Yeung, Alexander Ka Shing; Berendschot, Tos T. J. M.; Romeny, Bart M. ter Haar

doi:10.1007/978-3-319-68195-5_89

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…Many clinical studies have shown that the changes in retinal vessel caliber are associated with the progress of a variety of systemic diseases [ 5 , 17 , 18 , 19 ]. In this study, we measured the vessel width using the method proposed by Huang et al [ 20 , 21 ]. In brief, this technique utilizes the geodesic active contour model proposed by Caselles et al [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

Predicting CT-Based Coronary Artery Disease Using Vascular Biomarkers Derived from Fundus Photographs with a Graph Convolutional Neural Network

Huang

Lian

et al. 2022

Diagnostics

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The study population contains 145 patients who were prospectively recruited for coronary CT angiography (CCTA) and fundoscopy. This study first examined the association between retinal vascular changes and the Coronary Artery Disease Reporting and Data System (CAD-RADS) as assessed on CCTA. Then, we developed a graph neural network (GNN) model for predicting the CAD-RADS as a proxy for coronary artery disease. The CCTA scans were stratified by CAD-RADS scores by expert readers, and the vascular biomarkers were extracted from their fundus images. Association analyses of CAD-RADS scores were performed with patient characteristics, retinal diseases, and quantitative vascular biomarkers. Finally, a GNN model was constructed for the task of predicting the CAD-RADS score compared to traditional machine learning (ML) models. The experimental results showed that a few retinal vascular biomarkers were significantly associated with adverse CAD-RADS scores, which were mainly pertaining to arterial width, arterial angle, venous angle, and fractal dimensions. Additionally, the GNN model achieved a sensitivity, specificity, accuracy and area under the curve of 0.711, 0.697, 0.704 and 0.739, respectively. This performance outperformed the same evaluation metrics obtained from the traditional ML models (p < 0.05). The data suggested that retinal vasculature could be a potential biomarker for atherosclerosis in the coronary artery and that the GNN model could be utilized for accurate prediction.

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Section: Methodsmentioning

confidence: 99%

Predicting CT-Based Coronary Artery Disease Using Vascular Biomarkers Derived from Fundus Photographs with a Graph Convolutional Neural Network

Huang

Lian

et al. 2022

Diagnostics

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“…Measured on the arterioles coursing through a standard area 0.5 to 1.0 disc diameters from the optic disc margin. 12…”

Section: Labelmentioning

confidence: 99%

Approximation of a pipeline of unsupervised retina image analysis methods with a CNN

Heslinga

Pluim

Bozorg

et al. 2019

Medical Imaging 2019: Image Processing

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A pipeline of unsupervised image analysis methods for extraction of geometrical features from retinal fundus images has previously been developed. Features related to vessel caliber, tortuosity and bifurcations, have been identified as potential biomarkers for a variety of diseases, including diabetes and Alzheimer's. The current computationally expensive pipeline takes 24 minutes to process a single image, which impedes implementation in a screening setting. In this work, we approximate the pipeline with a convolutional neural network (CNN) that enables processing of a single image in a few seconds. As an additional benefit, the trained CNN is sensitive to key structures in the retina and can be used as a pretrained network for related disease classification tasks. Our model is based on the ResNet-50 architecture and outputs four biomarkers that describe global properties of the vascular tree in retinal fundus images. Intraclass correlation coefficients between the predictions of the CNN and the results of the pipeline showed strong agreement (0.86 -0.91) for three of four biomarkers and moderate agreement (0.42) for one biomarker. Class activation maps were created to illustrate the attention of the network. The maps show qualitatively that the activations of the network overlap with the biomarkers of interest, and that the network is able to distinguish venules from arterioles. Moreover, local high and low tortuous regions are clearly identified, confirming that a CNN is sensitive to key structures in the retina.

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Validation Study on Retinal Vessel Caliber Measurement Technique

Cited by 2 publications

References 15 publications

Predicting CT-Based Coronary Artery Disease Using Vascular Biomarkers Derived from Fundus Photographs with a Graph Convolutional Neural Network

Predicting CT-Based Coronary Artery Disease Using Vascular Biomarkers Derived from Fundus Photographs with a Graph Convolutional Neural Network

Approximation of a pipeline of unsupervised retina image analysis methods with a CNN

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