Retinal small vessel pathology is associated with disease burden in multiple sclerosis.

Background: Alterations of the superficial retinal vasculature are commonly observed in multiple sclerosis (MS) and can be visualized through optical coherence tomography angiography (OCTA).

Objectives: This study aimed to examine changes in the retinal vasculature during MS and to integrate findings into current concepts of the underlying pathology.

Methods: In this cross-sectional study, including 259 relapsing-remitting MS patients and 78 healthy controls, we analyzed OCTAs using deep-learning-based segmentation algorithm tools.

Retinal vasculature of different diameters and plexuses exhibit distinct vulnerability in varying severity of diabetic retinopathy.

Objectives: To study the changes in vessel densities (VD) stratified by vessel diameter in the retinal superficial and deep vascular complexes (SVC/DVC) using optical coherence tomography angiography (OCTA) images obtained from people with diabetes and age-matched healthy controls.

Methods: We quantified the VD based on vessel diameter categorized as <10, 10-20 and >20 μm in the SVC/DVC obtained on 3 × 3 mm OCTA scans using a deep learning-based segmentation and vascular graph extraction tool in people with diabetes and age-matched healthy controls.

Results: OCTA images obtained from 854 eyes of 854 subjects were divided into 5 groups: healthy controls (n = 555); people with diabetes with no diabetic retinopathy (DR, n = 90), mild and moderate non-proliferative DR (NPDR) (n = 96), severe NPDR (n = 42) and proliferative DR (PDR) (n = 71).

Synthetic Optical Coherence Tomography Angiographs for Detailed Retinal Vessel Segmentation Without Human Annotations.

Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that can acquire high-resolution volumes of the retinal vasculature and aid the diagnosis of ocular, neurological and cardiac diseases. Segmenting the visible blood vessels is a common first step when extracting quantitative biomarkers from these images. Classical segmentation algorithms based on thresholding are strongly affected by image artifacts and limited signal-to-noise ratio.