Retracted: Early diabetic retinopathy diagnosis based on local retinal blood vessels analysis in optical coherence tomography angiography (OCTA) images

Eladawi, Nabila; Elmogy, Mohammed; Khalifa, Fahmi; Ghazal, Mohammed; Ghazi, Nicola G.; Aboelfetouh, Ahmed; Riad, A. M.; Sandhu, Harpal S.; Schaal, Shlomit; El-Baz, Ayman

doi:10.1002/mp.12825

Cited by 6 publications

(1 citation statement)

References 46 publications

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“…The morphological and topographical changes of retinal vessels (RVs) might signify certain physical disorders. Diabetes can increase blood sugar and cause complications, 14 such as diabetic retinopathy (DR), which leads to the growth of new blood vessels. [15][16][17] Hypertensive retinopathy is another common retinal disease caused by high blood pressure.…”

Section: Introductionmentioning

confidence: 99%

RPS‐Net: An effective retinal image projection segmentation network for retinal vessels and foveal avascular zone based on OCTA data

Zhang

et al. 2022

Medical Physics

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Background Optical coherence tomography angiography (OCTA) is an advanced imaging technology that can present the three‐dimensional (3D) structure of retinal vessels (RVs). Quantitative analysis of retinal vessel density and foveal avascular zone (FAZ) area is of great significance in clinical diagnosis, and the automatic semantic segmentation at the pixel level helps quantitative analysis. The existing segmentation methods cannot effectively use the volume data and projection map data of the OCTA image at the same time and lack the trade‐off between global perception and local details, which lead to problems such as discontinuity of segmentation results and deviation of morphological estimation. Purpose In order to better assist physicians in clinical diagnosis and treatment, the segmentation accuracy of RVs and FAZ needs to be further improved. In this work, we propose an effective retinal image projection segmentation network (RPS‐Net) to achieve accurate RVs and FAZ segmentation. Experiments show that this network exhibits good performance and outperforms other existing methods. Methods Our method considers three aspects. First, we use two parallel projection paths to learn global perceptual features and local supplementary details. Second, we use the dual‐way projection learning module to reduce the depth of the 3D data and learn image spatial features. Finally, we merged the two‐dimensional features learned from the volume data with the two‐dimensional projection data, and used a U‐shaped network to further learn and generate the final result. Results We validated our model on the OCTA‐500, which is a large multi‐modal, multi‐task retinal dataset. The experimental results showed that our method achieved state‐of‐the‐art performance; the mean Dice coefficients for RVs are 89.89 ± 2.60 (%) and 91.40 ± 9.18 (%) on the two subsets, while the Dice coefficients for FAZ are 91.55 ± 2.05 (%) and 97.80 ± 2.75 (%), respectively. Conclusions Our method can make full use of the information of 3D data and 2D data to generate segmented images with higher continuity and accuracy. Code is available at https://github.com/hchuanZ/MFFN/tree/master.

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Section: Introductionmentioning

confidence: 99%

RPS‐Net: An effective retinal image projection segmentation network for retinal vessels and foveal avascular zone based on OCTA data

Zhang

et al. 2022

Medical Physics

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The relationship between retinal vessel geometrical changes to incidence and progression of Diabetic Macular Edema

Ajaz

Kumar²,

Aliahmad

et al. 2019

Informatics in Medicine Unlocked

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Quantification of Microvascular Density of the Optic Nerve Head in Diabetic Retinopathy Using Optical Coherence Tomographic Angiography

Huang

Zheng

et al. 2020

Journal of Ophthalmology

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Aims. To quantify the capillary density of the optic nerve head in healthy control eyes and different stages of diabetic retinopathy (DR) eyes and identify the parameters to detect eyes with or without DR using optical coherence tomographic angiography (OCTA). Methods. In this cross-sectional study, 211 eyes of 121 participants with type 2 diabetes with different stages of DR or without DR and 73 eyes of 38 healthy age-matched controls were imaged by OCTA. Radial peripapillary capillary (RPC) plexus density and retinal nerve fiber layer (RNFL) thickness were examined. The mixed model binary logistic regression model was used to identify the parameters to detect eyes with or without DR. The area under the receiver operating characteristic (ROC) curve was calculated. Results. RPC density decreased significantly in diabetic patients without DR compared with the healthy controls, and it was negatively correlated with the severity of DR (P<0.01). RPC density was a significant parameter to distinguish diabetic eyes with or without DR (P<0.01). The area under the ROC curve was 0.743. Conclusions. Quantification of RPC density by OCTA provides evidence of microvascular changes in the optic nerve in diabetic patients. RPC density can serve as a possible biomarker in detecting eyes with DR. Larger cohort studies need to support this statement.

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Retracted: Early diabetic retinopathy diagnosis based on local retinal blood vessels analysis in optical coherence tomography angiography (OCTA) images

Cited by 6 publications

References 46 publications

RPS‐Net: An effective retinal image projection segmentation network for retinal vessels and foveal avascular zone based on OCTA data

RPS‐Net: An effective retinal image projection segmentation network for retinal vessels and foveal avascular zone based on OCTA data

The relationship between retinal vessel geometrical changes to incidence and progression of Diabetic Macular Edema

Quantification of Microvascular Density of the Optic Nerve Head in Diabetic Retinopathy Using Optical Coherence Tomographic Angiography

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