2019
DOI: 10.1002/mp.13728
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Hyper‐reflective foci segmentation in SD‐OCT retinal images with diabetic retinopathy using deep convolutional neural networks

Abstract: Purpose The purpose of this study was to automatically and accurately segment hyper‐reflective foci (HRF) in spectral domain optical coherence tomography (SD‐OCT) images with diabetic retinopathy (DR) using deep convolutional neural networks. Methods An automatic HRF segmentation model for SD‐OCT images based on deep networks was constructed. The model segmented small lesions through pixel‐wise predictions based on small image patches. We used an approach for discriminative features extraction for small patche… Show more

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Cited by 21 publications
(18 citation statements)
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“…To explore the performance of the proposed framework, we compared our approach with some published methods. Figure 8 shows the comparison of the proposed method, two of the Okuwobi et al 12 , 13 methods (namely grow-cut-based method and component tree-based method), GoogLeNet based on patch-based classification, 14 ResUNet, 15 DUNet, 18 FCN, 19 and U-Net++. 20 The input of the Okuwobi et al methods 12 , 13 and GoogLeNet 14 are only input 1 because the Okuwobi et al methods are traditional methods and GoogLeNet is based on patch-based classification under the Caffe framework.…”
Section: Experimental Results and Analysismentioning
confidence: 99%
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“…To explore the performance of the proposed framework, we compared our approach with some published methods. Figure 8 shows the comparison of the proposed method, two of the Okuwobi et al 12 , 13 methods (namely grow-cut-based method and component tree-based method), GoogLeNet based on patch-based classification, 14 ResUNet, 15 DUNet, 18 FCN, 19 and U-Net++. 20 The input of the Okuwobi et al methods 12 , 13 and GoogLeNet 14 are only input 1 because the Okuwobi et al methods are traditional methods and GoogLeNet is based on patch-based classification under the Caffe framework.…”
Section: Experimental Results and Analysismentioning
confidence: 99%
“…Figure 8 shows the comparison of the proposed method, two of the Okuwobi et al 12,13 methods (namely grow-cut-based method and component tree-based method), GoogLeNet based on patchbased classification, 14 ResUNet, 15 DUNet, 18 FCN, 19 and U-Net++. 20 The input of the Okuwobi et al methods 12,13 and GoogLeNet 14 Figure 8, the results of method 12 and GoogLeNet have obvious undersegmentation. The component tree-based method 13 has obvious oversegmentation because it makes two HRF to one HRF.…”
Section: Comparison Against Existing Methodsmentioning
confidence: 99%
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