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Updated: Aug 6, 2026
Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies
Published on: March 12, 2022
Deep Learning-Guided Retinal Vascular Morphometric Quantification in Cerebral Autosomal Dominant Arteriopathy with
Hyungwoo Lee1,2, Na-Kyung Ryoo1, Said Arevalo-Alquichire1
1Schepens Eye Research Institute of Massachusetts Eye and Ear and Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts.
Purpose:
To develop and validate an explainable deep learning-guided workflow to localize and quantify focal retinal luminal pathology on fundus fluorescein angiography (FFA) in NOTCH3 variant knock-in mouse models of cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy.
Design:
Cross-sectional experimental imaging and computational analysis.
Subjects:
Thirty-two mice (wild-type [WT] n = 12; NOTCH3C455R [C455R] n = 12; NOTCH3R1031C [R1031C] n = 8) yielding 1670 analyzable FFA images.
Methods Intervention Or Testing:
Two ImageNet-pretrained VGG16 classifiers (WT vs. each mutant line) were trained with subject-grouped splitting. Grad-CAM++ and occlusion sensitivity maps defined class-discriminative regions of interest (ROIs). A centerline-based morphometry pipeline sampled luminal diameter along ordered vessel centerlines to compute mean and maximum diameter, diameter coefficient of variation, and tortuosity. A fast Fourier transform-derived vessel beading index (VBI) quantified periodic diameter oscillations using normalized, band-limited spectral power. Metrics were computed for large-vessel and small-vessel masks in both whole-field and ROI-restricted domains. Additional robustness analyses assessed hold-out testing, out-of-sample saliency, ROI-threshold sensitivity, alternative VBI spatial-period bands, and repeated balanced retraining.
Main Outcome Measures:
Primary biological outcomes were large-vessel mean diameter, maximum diameter, and VBI in whole-field and ROI-restricted analyses; classifier discrimination (area under the curve) was reported as supportive performance of the localization framework.
Results:
Whole-field morphometry detected generalized large-vessel dilation in both mutants versus WT (mean diameter: WT 27.82 μm; C455R 30.94 μm; R1031C 32.03 μm; P ≤ 0.001) with reduced tortuosity (P < 0.001), whereas whole-field maximum diameter and VBI increased only directionally. ROI-restricted analysis amplified focal pathology: within Grad-CAM++ ROIs, large-vessel maximum diameter increased (WT 37.62 μm; C455R 48.46 μm; R1031C 45.66 μm; P < 0.001) and VBI increased (WT 1.75; C455R 3.04; R1031C 2.93; P ≤ 0.001). Occlusion ROIs showed concordant VBI increases (WT 2.11; C455R 3.84; R1031C 4.90; P < 0.001). Small-vessel ROI differences were minimal. The principal large-vessel ROI-restricted phenotype remained directionally stable across robustness analyses, and hold-out testing confirmed high classifier discrimination in both genotype comparisons.
Conclusions:
Explainable deep learning-guided localization on FFA identifies disease-informative vessel segments and enables sensitive quantification of focal luminal dilation and periodic beading that are diluted by whole-field averages. This framework may support development of retinal biomarkers and longitudinal monitoring in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy.
Financial Disclosures:
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
