Evidence map›Paper›PMID 40634409›Full record

ArticleScientific reports2025

Low rank based FAZ segmentation in OCTA images.

Farnaz Sedighin, Fatemeh Rezaei, Maryam Monemian

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

The trial behind it

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Farnaz SedighinMedical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, 8174673461, Iran. f.sedighin@amt.mui.ac.ir.
Fatemeh RezaeiMedical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, 8174673461, Iran.
Maryam MonemianMedical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, 8174673461, Iran.

Funding

Isfahan University of Medical Sciences 2401199Isfahan University of Medical Sciences 2403178
6 · The paper itself

Abstract

Optical Coherence Tomography Angiography (OCTA) is a non-invasive method for vascular imaging of different tissues. Several diseases can disturb the blood supplying of retinal tissues which leads to loss of blood vessels and a reduction in the vascular density of retinal tissue. This also changes the size and shape of the FAZ (Fovea Avascular Zone) in the retinal OCTA images. Considering this fact, FAZ segmentation and analyzing its shape, can be used for diagnosing of several eye-related diseases. This paper is devoted to propose new methods for accurate segmentation of FAZ from OCTA images. The proposed methods have three steps: first, FAZ localization, for which, two methods have been proposed. The first one used Low-rank Tensor Ring (TR) decomposition of OCTA images and the other one exploited morphological operators. In the second step, the de-noised OCTA image is used for FAZ segmentation exploiting the information of FAZ localization, and in the third step, the resulting segmented FAZ is refined to derive a clean area. To the best of our knowledge, this is the first time of using low-rank TR estimation for FAZ segmentation. Simulation results confirmed the performance of the proposed methods for an accurate FAZ segmentation.

Indexed as

AngiographyFovea CentralisImage Processing, Computer-AssistedRetinal VesselsTomography, Optical CoherenceAlgorithmsHumansRetinaFAZ segmentationLow-rank estimationOptical coherence tomography angiographyTensor ring decomposition

Identifiers

PMID40634409
PMCPMC12241517

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