ArticleTranslational vision science & technology2026
Time Series Segmentation in Adaptive Optics Ophthalmoscopy With Application to Flicker-Induced Vasodilation.
Article in Translational vision science & technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To develop an automated image processing pipeline to segment retinal arterial walls in a series of adaptive optics ophthalmoscopy (AOO) images, overcoming difficulties related to various quality levels within the series, to accurately evaluate morphometric differences. Methods: We first assess the quality of AOO images by deep learning in order to select the best quality image in the sequence. Then, we segment this image by combining deep learning and dedicated active contour models. Finally, we segment the other images of the sequence using the best image segmentation as a regularization exploiting the temporal continuity to compensate for lower quality. Results: We applied the proposed method to flicker series, a challenging use-case given the image quality heterogeneity. The quality assessment method achieves a Spearman correlation coefficient of 0.86, demonstrating very good agreement with the quality ranking established by the medical expert. The automatic segmentation method results in accurate segmentations with a mean root mean square error of 0.5 pixel and 1.8 pixels for the inner and outer borders of the arterial wall, respectively. This leads to accurate and unbiased estimates of the wall to lumen ratio. Conclusions: Our algorithm can be applied as a reliable and useful computer-aided tool to process large time series databases in AOO, and can help experts in longitudinal monitoring applications. Translational Relevance: For the first time, AOO image series can be processed with low supervision to accurately monitor morphometric changes in retinal vascularization.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.