Infrastructure for Retinal Image Analysis

Dashtbozorg, Behdad; Abbasi-Sureshjani, Samaneh; Zhang, Jiong; Huang, Fan; Bekkers, Erik J.; Romeny, Bart ter Haar

doi:10.17077/omia.1053

Cited by 5 publications

(6 citation statements)

References 14 publications

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“…However, the results in the paper show that the features within the image are extractable. There has also been similar orientation-based work in [7,8].…”

Section: Introductionmentioning

confidence: 85%

Automatic Detection and Distinction of Retinal Vessel Bifurcations and Crossings in Colour Fundus Photography

Pratt

Williams

et al. 2017

J. Imaging

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Abstract:The analysis of retinal blood vessels present in fundus images, and the addressing of problems such as blood clot location, is important to undertake accurate and appropriate treatment of the vessels. Such tasks are hampered by the challenge of accurately tracing back problems along vessels to their source. This is due to the unresolved issue of distinguishing automatically between vessel bifurcations and vessel crossings in colour fundus photographs. In this paper, we present a new technique for addressing this problem using a convolutional neural network approach to firstly locate vessel bifurcations and crossings and then to classifying them as either bifurcations or crossings. Our method achieves high accuracies for junction detection and classification on the DRIVE dataset and we show further validation on an unseen dataset from which no data has been used for training. Combined with work in automated segmentation, this method has the potential to facilitate: reconstruction of vessel topography, classification of veins and arteries and automated localisation of blood clots and other disease symptoms leading to improved management of eye disease.

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“…However, the results in the paper show that the features within the image are extractable. There has also been similar orientation-based work in [7,8].…”

Section: Introductionmentioning

confidence: 85%

Automatic Detection and Distinction of Retinal Vessel Bifurcations and Crossings in Colour Fundus Photography

Pratt

Williams

et al. 2017

J. Imaging

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“…In previous works [5,7,10] of the RetinaCheck project, several retinal image analysis tools including automatic retinal vessel enhancement [1], segmentation [19][20][21], optic disc/fovea detection [8], artery/vein classification [10], caliber calculation [5], vessel curvature measurement [3] and fractal analysis [12,13] have been implemented to obtain important vessel biomarkers like central retinal arterial equivalent (CRAE), central retinal venous equivalent (CRVE), the arterial-venous diameter ratio (AVR), vessel tortuosity and fractal dimension. In Fig.…”

Section: Methodsmentioning

confidence: 99%

“…In RetintaCheck project, 1 a retinal image analysis infrastructure is set up for the automated detection and segmentation of important retinal landmarks, as well as for the assessment of vascular changes [5]. Subsequently, important vessel-based biomarkers are automatically computed from retinal images in a repeatable and objective manner.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Retinal Vascular Biomarkers for Early Detection of Diabetes

et al. 2017

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“…Others have focused on the extraction of handcrafted features from fundus images. 10,11 Features such as vessel tortuosity, mean arteriolar width and venular width are considered biomarkers for T2D. 7 In previous work we showed that these biomarkers can be approximated with a deep learning approach.…”

Section: Related Workmentioning

confidence: 99%

Direct classification of type 2 diabetes from retinal fundus images in a population-based sample from The Maastricht Study

Heslinga

Pluim

Houben

et al. 2020

Medical Imaging 2020: Computer-Aided Diagnosis

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Type 2 Diabetes (T2D) is a chronic metabolic disorder that can lead to blindness and cardiovascular disease. Information about early stage T2D might be present in retinal fundus images, but to what extent these images can be used for a screening setting is still unknown. In this study, deep neural networks were employed to differentiate between fundus images from individuals with and without T2D. We investigated three methods to achieve high classification performance, measured by the area under the receiver operating curve (ROC-AUC). A multi-target learning approach to simultaneously output retinal biomarkers as well as T2D works best (AUC = 0.746 [±0.001]). Furthermore, the classification performance can be improved when images with high prediction uncertainty are referred to a specialist. We also show that the combination of images of the left and right eye per individual can further improve the classification performance (AUC = 0.758 [±0.003]), using a simple averaging approach. The results are promising, suggesting the feasibility of screening for T2D from retinal fundus images.

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Infrastructure for Retinal Image Analysis

Cited by 5 publications

References 14 publications

Automatic Detection and Distinction of Retinal Vessel Bifurcations and Crossings in Colour Fundus Photography

Automatic Detection and Distinction of Retinal Vessel Bifurcations and Crossings in Colour Fundus Photography

Analysis of Retinal Vascular Biomarkers for Early Detection of Diabetes

Direct classification of type 2 diabetes from retinal fundus images in a population-based sample from The Maastricht Study

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