Image Analysis for Ophthalmology: Segmentation and Quantification of Retinal Vascular Systems

Palaniappan, Kannappan; Bunyak, Filiz; Chaurasia, Shyam S.

doi:10.1007/978-3-030-25886-3_22

Cited by 4 publications

(2 citation statements)

References 141 publications

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“…In the present study, the vessel segmentation traces and flows shown in Fig. 1 were manually analysed to provide gold standard architecture networks for the simulation models and can be used as training data for automated computer vision methods (Kassim et al 2019;Palaniappan et al 2019;Kassim et al 2021). Once the microvascular network graph is constructed and capillary vessel segment types identified, we utilised a Generalised Voronoi Diagram (GVD) to estimate tissue perfusion regions.…”

Section: Estimation Of Tissue Perfusion Areasmentioning

confidence: 99%

Fluid and protein exchange in microvascular networks: Importance of modelling heterogeneity in geometrical and biophysical properties

Guidoboni

Marazzi

Fraser

et al. 2021

The Journal of Physiology

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support-information-section). Key pointsr Microvascular network architecture defines coupling of fluid and protein exchange. r Network arrangements markedly reduce capillary hydrostatic pressures and resting fluid movement at the same time as increasing the capacity for change r The presence of vascular remodelling or angiogenesis puts constraints of network behaviour r The sites of fluid and protein exchange can be segregated to different portions of the network r Although there is a net filtration of fluid from a network of exchange vessels, there are specific areas where fluid moves into the circulation (reabsorption) and, when protein is moving into tissue, the amount is insufficient under basal conditions to result in changes in oncotic pressure.

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Section: Estimation Of Tissue Perfusion Areasmentioning

confidence: 99%

Fluid and protein exchange in microvascular networks: Importance of modelling heterogeneity in geometrical and biophysical properties

Guidoboni

Marazzi

Fraser

et al. 2021

The Journal of Physiology

Self Cite

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“…FA is a standard diagnostic tool for fundus diseases, which allows dynamic observation of retinal vascular circulation using fluorescein under physiological and pathological conditions [ 18 ]. FA can be divided into prefilling, transit, recirculation, and late phases.…”

Section: Introductionmentioning

confidence: 99%

Synthesizing multi-frame high-resolution fluorescein angiography images from retinal fundus images using generative adversarial networks

Wang

et al. 2023

BioMed Eng OnLine

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Background Fundus fluorescein angiography (FA) can be used to diagnose fundus diseases by observing dynamic fluorescein changes that reflect vascular circulation in the fundus. As FA may pose a risk to patients, generative adversarial networks have been used to convert retinal fundus images into fluorescein angiography images. However, the available methods focus on generating FA images of a single phase, and the resolution of the generated FA images is low, being unsuitable for accurately diagnosing fundus diseases. Methods We propose a network that generates multi-frame high-resolution FA images. This network consists of a low-resolution GAN (LrGAN) and a high-resolution GAN (HrGAN), where LrGAN generates low-resolution and full-size FA images with global intensity information, HrGAN takes the FA images generated by LrGAN as input to generate multi-frame high-resolution FA patches. Finally, the FA patches are merged into full-size FA images. Results Our approach combines supervised and unsupervised learning methods and achieves better quantitative and qualitative results than using either method alone. Structural similarity (SSIM), normalized cross-correlation (NCC) and peak signal-to-noise ratio (PSNR) were used as quantitative metrics to evaluate the performance of the proposed method. The experimental results show that our method achieves better quantitative results with structural similarity of 0.7126, normalized cross-correlation of 0.6799, and peak signal-to-noise ratio of 15.77. In addition, ablation experiments also demonstrate that using a shared encoder and residual channel attention module in HrGAN is helpful for the generation of high-resolution images. Conclusions Overall, our method has higher performance for generating retinal vessel details and leaky structures in multiple critical phases, showing a promising clinical diagnostic value.

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Artificial intelligence in retinal image analysis: Development, advances, and challenges

Oganov¹,

Seddon²,

Jabbehdari³

et al. 2023

Survey of Ophthalmology

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Image Analysis for Ophthalmology: Segmentation and Quantification of Retinal Vascular Systems

Cited by 4 publications

References 141 publications

Fluid and protein exchange in microvascular networks: Importance of modelling heterogeneity in geometrical and biophysical properties

Fluid and protein exchange in microvascular networks: Importance of modelling heterogeneity in geometrical and biophysical properties

Synthesizing multi-frame high-resolution fluorescein angiography images from retinal fundus images using generative adversarial networks

Artificial intelligence in retinal image analysis: Development, advances, and challenges

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