Time Series Segmentation in Adaptive Optics Ophthalmoscopy With Application to Flicker-Induced Vasodilation.
6 affiliations
- 1Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
- 2Télécom Paris, LTCI, Institut Polytechnique de Paris, Palaiseau, France.
- 3Isep, Institut Supérieur d'Electronique de Paris, Paris, France.
- 4Sorbonne Université, CNRS, LIP6, Paris, France.
- 5Clinical Investigation Center 1423, Quinze-Vingts Hospital, Sorbonne Université, INSERM, Paris, France.
- 6Referral Centre CERVCO at the University Hospital Lariboisière in APHP (University Paris Cité), France.
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.
Related
- Use of a Retinal Phantom to Facilitate Adaptive Optics Scanning Light Ophthalmoscopy Imaging in a Multicenter Clinical Trial
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- High-Resolution Characterization of Cone Photoreceptor Mosaic Alterations in Retinopathy of Prematurity Using Adaptive Optics Scanning Laser Ophthalmoscopy
- AMF-U-Net: an adaptive multimodal fusion residual attention 3D U-Net for boundary-aware brain tumour segmentation
BibTeX
@article{vard2026time,
title = {{Time Series Segmentation in Adaptive Optics Ophthalmoscopy With Application to Flicker-Induced Vasodilation.}},
author = {Alireza Vard and Abir Aissa and Florence Rossant and Isabelle Bloch and Daniela Castro-Farias and Hugues Chabriat and Michel Paques},
journal = {Translational vision science \& technology},
year = {2026},
month = oct,
doi = {10.1167/tvst.15.10.8},
url = {https://europepmc.org/article/MED/42841580},
note = {CC BY},
}
RIS
TY - JOUR
TI - Time Series Segmentation in Adaptive Optics Ophthalmoscopy With Application to Flicker-Induced Vasodilation.
AU - Alireza Vard
AU - Abir Aissa
AU - Florence Rossant
AU - Isabelle Bloch
AU - Daniela Castro-Farias
AU - Hugues Chabriat
AU - Michel Paques
PY - 2026
DA - 2026/10/01
JO - Translational vision science & technology
DO - 10.1167/tvst.15.10.8
UR - https://europepmc.org/article/MED/42841580
AB - 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.
SN - 2164-2591
ER -