JOINT MOTION CORRECTION AND 3D SEGMENTATION WITH GRAPH-ASSISTED NEURAL NETWORKS FOR RETINAL OCT.

Yiqian Wang, Carlo Galang, William R Freeman, Truong Q Nguyen, Cheolhong An

Abstract read

In one paragraph

Article in Proceedings. International Conference on Image Processing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

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

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Retinal OCT Layer Segmentation via Joint Motion Correction and Graph-Assisted 3D Neural Network.IEEE access : practical innovations, open solutions · 2023
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yiqian WangDepartment of Electrical and Computer Engineering, University of California, San Diego.

Carlo GalangJacobs Retina Center, Shiley Eye Institute, University of California, San Diego.

William R FreemanJacobs Retina Center, Shiley Eye Institute, University of California, San Diego.

Truong Q NguyenDepartment of Electrical and Computer Engineering, University of California, San Diego.

Cheolhong AnDepartment of Electrical and Computer Engineering, University of California, San Diego.

Funding

SCH: Multimodal Retina Image Alignment and ApplicationsR01EY033847 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI AN, CHEOLHONG, BARTSCH, DIRK-UWE G · 2021 to 2024

$1.2M

NEI NIH HHS R01 EY033847

6 · The paper itself

Abstract

Optical Coherence Tomography (OCT) is a widely used non-invasive high resolution 3D imaging technique for biological tissues and plays an important role in ophthalmology. OCT retinal layer segmentation is a fundamental image processing step for OCT-Angiography projection, and disease analysis. A major problem in retinal imaging is the motion artifacts introduced by involuntary eye movements. In this paper, we propose neural networks that jointly correct eye motion and retinal layer segmentation utilizing 3D OCT information, so that the segmentation among neighboring B-scans would be consistent. The experimental results show both visual and quantitative improvements by combining motion correction and 3D OCT layer segmentation comparing to conventional and deep-learning based 2D OCT layer segmentation.

Indexed as

deep learningimage segmentationmotion correctionOptical coherence tomographyretinal imaging

Identifiers

PMID37342228

DOI10.1109/icip46576.2022.9898072

PMCPMC10280808

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Registered trials

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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.