Browsing by Subject "Retinal vasculature"

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    Relationship of retinal vasculature with measures of amyloid, tau, and neurodegeneration across the AD continuum
    (Wiley, 2025-01-09) Mathew, Sunu; Mackay, Devin; Tallman, Eileen F.; Deardorff, Rachael; Hottle, Savannah; Vosmeier, Aaron; Clark, David G.; Farlow, Martin R.; Brosch, Jared R.; Gao, Sujuan; Apostolova, Liana G.; Saykin, Andrew J.; Risacher, Shannon L.; Neurology, School of Medicine
    Background: The eye often reflects changes seen in the brain in neurodegenerative diseases. This study sought to examine the relationship of retinal vasculature measured using optical coherence tomography angiography (OCTA) with temporal lobe neurodegeneration, and cerebral amyloid and tau deposition, in older adults along the Alzheimer’s disease (AD) continuum. Method: Participants included 13 cognitively normal subjects, 5 with subjective cognitive decline (SCD), 7 with cognitive impairment (mild cognitive impairment [MCI] and AD) from the Indiana Memory and Aging Study at the Indiana ADRC. Participants were excluded from the study if they had significant eye disease determined to interfere with OCTA, non‐AD dementia, or exclusion for MRI or PET. OCTA scans were obtained from each eye to measure retinal vessel density and perfusion density. MRI scans were processed using Freesurfer v6 to measure medial (MTL) and lateral temporal lobe (LTL) volumes. LTL SUVR values were extracted from [18F]flortaucipir PET scans. Finally, the association between retinal perfusion and vessel density with hippocampal volume, MTL tau, and lateral parietal amyloid was assessed using a partial Pearson correlation, covaried for age, sex, and diagnosis. p<0.05 was considered significant. Result: Retinal vessel and perfusion density were decreased in patients with AD. The right and left hippocampal volume were significantly correlated with retinal vessel density and perfusion density in the right eye and left hippocampal volume was correlated with vessel density in the left eye, but it did not reach significance. The retinal vessel density and perfusion density in the right eye correlated significantly with lateral parietal lobe amyloid and medial temporal lobe tau. Finally, the total gray matter volume correlated significantly with the retinal vessel density and perfusion density in the right eye and inversely with the foveal avascular zone in the right eye. Conclusion: Retinal perfusion and vessel density correlates with hippocampal atrophy, and general atrophy of the gray matter. It is also significantly correlated with the deposition of amyloid and tau in the brain. Imaging the retinal vasculature may represent a useful biomarker to screen patients at risk for AD prior to more invasive and prolonged testing.
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    Unsupervised automated retinal vessel segmentation based on Radon line detector and morphological reconstruction
    (Wiley, 2021) Tavakoli, Meysam; Mehdizadeh, Alireza; Pourreza Shahri, Reza; Dehmeshki, Jamshid; Physics, School of Science
    Retinal blood vessel segmentation and analysis is critical for the computer-aided diagnosis of different diseases such as diabetic retinopathy. This study presents an automated unsupervised method for segmenting the retinal vasculature based on hybrid methods. The algorithm initially applies a preprocessing step using morphological operators to enhance the vessel tree structure against a non-uniform image background. The main processing applies the Radon transform to overlapping windows, followed by vessel validation, vessel refinement and vessel reconstruction to achieve the final segmentation. The method was tested on three publicly available datasets and a local database comprising a total of 188 images. Segmentation performance was evaluated using three measures: accuracy, receiver operating characteristic (ROC) analysis, and the structural similarity index. ROC analysis resulted in area under curve values of 97.39%, 97.01%, and 97.12%, for the DRIVE, STARE, and CHASE-DB1, respectively. Also, the results of accuracy were 0.9688, 0.9646, and 0.9475 for the same datasets. Finally, the average values of structural similarity index were computed for all four datasets, with average values of 0.9650 (DRIVE), 0.9641 (STARE), and 0.9625 (CHASE-DB1). These results compare with the best published results to date, exceeding their performance for several of the datasets; similar performance is found using accuracy.