High dynamic range widefield fundus photography with transcranial illumination: a contrast agent-free method for non-mydriatic choroidal imaging.

The choroid, a critical vascular layer beneath the retina, is essential for maintaining retinal function and monitoring chorioretinal disorders. Existing imaging methods, such as indocyanine green angiography (ICGA) and optical coherence tomography (OCT), face significant limitations, including contrast agent requirements, restricted field of view (FOV), and high costs, limiting accessibility. To address these challenges, we developed a nonmydriatic, contrast agent-free fundus camera utilizing transcranial near-infrared (NIR) illumination.

OCTA-ReVA: an open-source toolbox for comprehensive retinal vessel feature analysis in optical coherence tomography angiography.

Optical coherence tomography angiography (OCTA) has significantly advanced the study and diagnosis of eye diseases. However, current clinical OCTA systems and software tools lack comprehensive quantitative analysis capabilities, limiting their full clinical utility. This paper introduces the OCTA Retinal Vessel Analyzer (OCTA-ReVA), a versatile open-source platform featuring a user-friendly graphical interface designed for the automated extraction and quantitative analysis of OCTA features.

Adaptive vessel tracing and segmentation in OCT enables the robust detection of wall-to-lumen ratio abnormalities in 5xFAD mice.

The wall-to-lumen ratio (WLR) of retinal blood vessels promises a sensitive marker for the physiological assessment of eye conditions. However, measurement of vessel wall thickness and lumen diameter is still technically challenging, hindering the wide application of WLR in research and clinical settings. In this study, we demonstrate the feasibility of using optical coherence tomography (OCT) as one practical method for quantification of WLR in the retina.

Intrinsic signal optoretinography revealing AD-induced retinal photoreceptor hyperexcitability before a detectable morphological abnormality.

Neuronal hyperexcitability promises an early biomarker of Alzheimer's disease (AD). However, in vivo detection of neuronal hyperexcitability in the brain is technically challenging. The retina, one part of the central nervous system, presents a unique window for noninvasive monitoring of the brain function.

Deep learning for artery-vein classification in optical coherence tomography angiography.

Major retinopathies can differentially impact the arteries and veins. Traditional fundus photography provides limited resolution for visualizing retinal vascular details. Optical coherence tomography (OCT) can provide improved resolution for retinal imaging.

Rotational Distortion and Compensation in Optical Coherence Tomography with Anisotropic Pixel Resolution.

Accurate image registration is essential for eye movement compensation in optical coherence tomography (OCT) and OCT angiography (OCTA). The spatial resolution of an OCT instrument is typically anisotropic, i.e.

ADC-Net: An Open-Source Deep Learning Network for Automated Dispersion Compensation in Optical Coherence Tomography.

Chromatic dispersion is a common problem to degrade the system resolution in optical coherence tomography (OCT). This study is to develop a deep learning network for automated dispersion compensation (ADC-Net) in OCT. The ADC-Net is based on a modified UNet architecture which employs an encoder-decoder pipeline.

Depth-resolved vascular profile features for artery-vein classification in OCT and OCT angiography of human retina.

This study is to characterize reflectance profiles of retinal blood vessels in optical coherence tomography (OCT), and to test the potential of using these vascular features to guide artery-vein classification in OCT angiography (OCTA) of the human retina. Depth-resolved OCT reveals unique features of retinal arteries and veins. Retinal arteries show hyper-reflective boundaries at both upper (inner side towards the vitreous) and lower (outer side towards the choroid) walls.

Systematic Review of Generative Adversarial Networks (GANs) for Medical Image Classification and Segmentation.

In recent years, generative adversarial networks (GANs) have gained tremendous popularity for various imaging related tasks such as artificial image generation to support AI training. GANs are especially useful for medical imaging-related tasks where training datasets are usually limited in size and heavily imbalanced against the diseased class. We present a systematic review, following the PRISMA guidelines, of recent GAN architectures used for medical image analysis to help the readers in making an informed decision before employing GANs in developing medical image classification and segmentation models.