Near infrared oximetry-guided artery–vein classification in optical coherence tomography angiography

Son, Taeyoon; Alam, Minhaj Nur; Kim, Tae-Hoon; Liu, Changgeng; Toslak, Devrim; Yao, Xincheng

doi:10.1177/1535370219850791

Cited by 20 publications

(13 citation statements)

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“…Employing image registration for the fundus and corresponding OCTA images, a vessel tracking algorithm is implemented to generate an A–V map in OCTA guided by the fundus A–V map. 41 In addition to the color fundus image guided A–V differentiation in OCTA, OCT feature analysis guided A–V differentiation 53 and near infrared oximetry guided A–V differentiation 54 have been also demonstrated.…”

Section: Quantitative Octa Featuresmentioning

confidence: 99%

Quantitative optical coherence tomography angiography: A review

Yao

Alam

et al. 2020

Exp Biol Med (Maywood)

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As a new optical coherence tomography (OCT) modality, OCT angiography (OCTA) provides a noninvasive method to detect microvascular distortions correlated with eye conditions. By providing unparalleled capability to differentiate individual plexus layers in the retina, OCTA has demonstrated its excellence in clinical management of diabetic retinopathy, glaucoma, sickle cell retinopathy, diabetic macular edema, and other eye diseases. Quantitative OCTA analysis of retinal and choroidal vasculatures is essential to standardize objective interpretations of clinical outcome. Quantitative features, including blood vessel tortuosity, blood vessel caliber, blood vessel density, vessel perimeter index, fovea avascular zone area, fovea avascular zone contour irregularity, vessel branching coefficient, vessel branching angle, branching width ratio, and choroidal vascular analysis have been established for objective OCTA assessment. Moreover, differential artery–vein analysis has been recently demonstrated to improve OCTA performance for objective detection and classification of eye diseases. In this review, technical rationales and clinical applications of these quantitative OCTA features are summarized, and future prospects for using these quantitative OCTA features for artificial intelligence classification of eye conditions are discussed. Impact statement OCT angiography (OCTA) provides a noninvasive method to detect microvascular distortions correlated with eye conditions. Quantitative analysis of OCTA is essential to standardize objective interpretations of clinical outcome. This review summarizes technical rationales and clinical applications of quantitative OCTA features.

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Section: Quantitative Octa Featuresmentioning

confidence: 99%

Quantitative optical coherence tomography angiography: A review

Yao

Alam

et al. 2020

Exp Biol Med (Maywood)

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“…Especially, OCTA is sensitive to identify subtle microvascular changes, and thus have been extensively explored for quantitative analysis and objective classification of retinal diseases [20][21][22][23][24]. Using quantitative feature analysis, we have recently demonstrated the potential of differentiating artery and vein in OCTA [4,5,25,26]. Differential AV analysis showed improved OCTA performance to identify abnormal changes in diabetic retinopathy (DR) and sickle cell retinopathy (SCR) eyes [4,5,26].…”

Section: Introductionmentioning

confidence: 99%

“…Using quantitative feature analysis, we have recently demonstrated the potential of differentiating artery and vein in OCTA [4,5,25,26]. Differential AV analysis showed improved OCTA performance to identify abnormal changes in diabetic retinopathy (DR) and sickle cell retinopathy (SCR) eyes [4,5,26]. However, clinical deployment of the AV analysis in OCTA requires an automated, simple, but robust method.…”

Section: Introductionmentioning

confidence: 99%

AV-Net: deep learning for fully automated artery-vein classification in optical coherence tomography angiography

Alam¹,

Le²,

Son³

et al. 2020

Biomed. Opt. Express

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This study is to demonstrate deep learning for automated artery-vein (AV) classification in optical coherence tomography angiography (OCTA). The AV-Net, a fully convolutional network (FCN) based on modified U-shaped CNN architecture, incorporates enface OCT and OCTA to differentiate arteries and veins. For the multi-modal training process, the enface OCT works as a near infrared fundus image to provide vessel intensity profiles, and the OCTA contains blood flow strength and vessel geometry features. A transfer learning process is also integrated to compensate for the limitation of available dataset size of OCTA, which is a relatively new imaging modality. By providing an average accuracy of 86.75%, the AV-Net promises a fully automated platform to foster clinical deployment of differential AV analysis in OCTA.

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“…For this reason, artery/vein differentiation in OCTA images has usually been supplemented with other imaging modalities, for example, oximetry. 13 , 35 However, combinations of multiple imaging modalities introduce a large burden into clinical practice and so should be avoided if a result can be achieved using just a single imaging technique. Our network achieves artery/vein differentiation using only a single OCT angiogram as input.…”

Section: Discussionmentioning

confidence: 99%