Research graph
References from Time Series Segmentation in Adaptive Optics Ophthalmoscopy With Application to Flicker-Induced Vasodilation. Local targets link to admitted publications; unresolved targets remain external evidence.
Adaptive optics ophthalmoscopy: a systematic review of vascular biomarkers
10.1016/j.survophthal.2021.05.012 · 2022 · External reference
Adaptive optics ophthalmoscopy: application to age-related macular degeneration and vascular diseases
10.1016/j.preteyeres.2018.07.001 · 2018 · External reference
Structural and functional analysis of retinal vasculature in HANAC syndrome with a novel intronic COL4A1 mutation
10.1016/j.mvr.2022.104450 · 2023 · External reference
Morphometric analysis of small arteries in the human retina using adaptive optics imaging: relationship with blood pressure and focal vascular changes
10.1097/hjh.0000000000000095 · 2014 · External reference
Early remodeling and loss of light-induced dilation of retinal small arteries in CADASIL
10.1177/0271678x241226484 · 2024 · External reference
Dilation of small retinal vessels during full-field flicker stimulation
2024 · External reference
High-resolution structural and functional retinal imaging in the awake behaving mouse
10.1038/s42003-023-04896-x · 2023 · External reference
Chaining a U-Net with a residual U-Net for retinal blood vessels segmentation
10.1109/access.2020.2975745 · 2020 · External reference
A global and local enhanced residual U-Net for accurate retinal vessel segmentation
10.1109/tcbb.2019.2917188 · 2019 · External reference
Feature pyramid U-Net for retinal vessel segmentation
10.1049/ipr2.12142 · 2021 · External reference
Attention mechanisms in computer vision: a survey
10.1007/s41095-022-0271-y · 2022 · External reference
A retinal vessel segmentation method based on an improved U-Net model
10.1016/j.bspc.2023.104574 · 2023 · External reference
CCS-UNet: a cross-channel spatial attention model for accurate retinal vessel segmentation
10.1364/boe.495766 · 2023 · External reference
CoVi-Net: A hybrid convolutional and vision transformer neural network for retinal vessel segmentation
10.1016/j.compbiomed.2024.108047 · 2024 · External reference
TCDDU-Net: combining transformer and convolutional dual-path decoding U-Net for retinal vessel segmentation
10.1038/s41598-024-77464-w · 2024 · External reference
UGS-M3F: unified gated Swin transformer with multi-feature fully fusion for retinal blood vessel segmentation
10.1186/s12880-025-01616-1 · 2025 · External reference
Parallel double snakes: application to the segmentation of retinal layers in 2D-OCT for pathological subjects
10.1016/j.patcog.2015.06.009 · 2015 · External reference
A fully automatic method for segmenting retinal artery walls in adaptive optics images
10.1016/j.patrec.2015.10.011 · 2016 · External reference
Characterization of retinal arteries by adaptive optics ophthalmoscopy image analysis
10.1109/tbme.2024.3408232 · 2024 · External reference
Revealing neurovascular coupling at a high spatial and temporal resolution in the living human retina
10.1126/sciadv.adx2941 · 2025 · External reference
Adaptive optics imaging of geographic atrophy
10.1167/iovs.12-10672 · 2013 · External reference
Follow-up of morphometric parameters of retinal arterioles in hypertensive subjects: an adaptive optics imaging study
2015 · External reference
Effects of age, blood pressure and antihypertensive treatments on retinal arterioles remodeling assessed by adaptive optics
10.1097/hjh.0000000000000894 · 2016 · External reference
Adaptive optics imaging in diabetic retinopathy: a comprehensive review
2025 · External reference
Snakes: active contour models
10.1007/bf00133570 · 1988 · External reference
Toward image quality assessment in mammography using model observers: detection of a calcification-like object
10.1002/mp.12532 · 2017 · External reference
Automated diabetic retinopathy image assessment software: diagnostic accuracy and cost-effectiveness compared with human graders
10.1016/j.ophtha.2016.11.014 · 2017 · External reference
Automated retinal image quality assessment on the UK Biobank dataset for epidemiological studies
10.1016/j.compbiomed.2016.01.027 · 2016 · External reference
FUIQA: fetal ultrasound image quality assessment with deep convolutional networks
10.1109/tcyb.2017.2671898 · 2017 · External reference
Retinal image quality assessment using deep learning
10.1016/j.compbiomed.2018.10.004 · 2018 · External reference
Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening
10.1016/j.media.2006.09.006 · 2006 · External reference
Suitability of UK Biobank retinal images for automatic analysis of morphometric properties of the vasculature
10.1371/journal.pone.0127914 · 2015 · External reference
Automated quality evaluation of digital fundus photographs
10.1111/j.1755-3768.2008.01321.x · 2009 · External reference
Retinal image quality assessment using generic image quality indicators
10.1016/j.inffus.2012.08.001 · 2014 · External reference
Automated quality assessment of retinal fundus photos
10.1007/s11548-010-0479-7 · 2010 · External reference
Performance dependency of retinal image quality assessment algorithms on image resolution: analyses and solutions
10.1007/s11760-017-1124-5 · 2018 · External reference
Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization
10.1038/s41746-019-0096-y · 2019 · External reference
Automated image quality evaluation of structural brain MRI using an ensemble of deep learning networks
10.1002/jmri.26693 · 2019 · External reference
A deep learning framework for quality assessment and restoration in video endoscopy
10.1016/j.media.2020.101900 · 2021 · External reference
Deep learning for image quality assessment of fundus images in retinopathy of prematurity
2018 · External reference
Deep learning for quality assessment of retinal OCT images
10.1364/boe.10.006057 · 2019 · External reference
Very deep convolutional networks for large-scale image recognition
2014 · External reference
MobileNets: efficient convolutional neural networks for mobile vision applications
2017 · External reference
Snakes, shapes, and gradient vector flow
10.1109/83.661186 · 1998 · External reference
Review of medical image quality assessment
10.1016/j.bspc.2016.02.006 · 2016 · External reference
Automated fundus image quality assessment in retinopathy of prematurity using deep convolutional neural networks
10.1016/j.oret.2019.01.015 · 2019 · External reference
No-reference image quality assessment of magnetic resonance images with multi-level and multi-model representations based on fusion of deep architectures
10.1016/j.engappai.2023.106283 · 2023 · External reference
U-Net: Convolutional networks for biomedical image segmentation
2015 · External reference
Residual U-Net for retinal vessel segmentation
2019 · External reference
Pyramid U-Net for retinal vessel segmentation
2021 · External reference
CBAM: Convolutional block attention module
2018 · External reference
U-Net with attention mechanism for retinal vessel segmentation
10.1007/978-3-030-34110-7_56 · 2019 · External reference
SA-UNet: Spatial attention U-Net for retinal vessel segmentation
2021 · External reference
Fully automatic CNN-based segmentation of retinal bifurcations in 2D adaptive optics ophthalmoscopy images
2020 · External reference
Deep learning-based segmentation of retinal vessels in adaptive optics ophthalmoscopy images
2024 · External reference
Segmentation of retinal arterial bifurcations in 2D adaptive optics ophthalmoscopy images
2019 · External reference
Unresolved reference
External reference
Unresolved reference
1983 · External reference
Image quality assessment
10.1016/b978-0-08-102816-2.00008-3 · 2019 · External reference
Evaluation of retinal image quality assessment networks in different color-spaces
2019 · External reference
Rethinking the inception architecture for computer vision
2016 · External reference
Densely connected convolutional networks
2017 · External reference
Xception: deep learning with depthwise separable convolutions
2017 · External reference
Inception-v4, Inception-ResNet and the impact of residual connections on learning
2017 · External reference
Deep residual learning for image recognition
2016 · External reference
ImageNet: a large-scale hierarchical image database
2009 · External reference
Encoder-decoder with atrous separable convolution for semantic image segmentation
2018 · External reference
Unresolved reference
1999 · External reference
