Evidence map›Paper›PMID 35950046›Full record

ArticleProceedings. International Conference on Image Processing2021

LEARNING TO CORRECT AXIAL MOTION IN OCT FOR 3D RETINAL IMAGING.

Yiqian Wang, Alexandra Warter, Melina Cavichini-Cordeiro, William R Freeman, Dirk-Uwe G Bartsch, Truong Q Nguyen, Cheolhong An

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings. International Conference on Image Processing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. JOINT MOTION CORRECTION AND 3D SEGMENTATION WITH GRAPH-ASSISTED NEURAL NETWORKS FOR RETINAL OCT.Proceedings. International Conference on Image Processing · 2022
    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

7 authors.

Yiqian WangDepartment of Electrical and Computer Engineering, University of California, San Diego.
Alexandra WarterJacobs Retina Center, Shiley Eye Institute, La Jolla, California, USA.
Melina Cavichini-CordeiroJacobs Retina Center, Shiley Eye Institute, La Jolla, California, USA.
William R FreemanJacobs Retina Center, Shiley Eye Institute, La Jolla, California, USA.
Dirk-Uwe G BartschJacobs Retina Center, Shiley Eye Institute, La Jolla, California, USA.
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

Vision BiostatisticsP30EY022589 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Derek Stuart Welsbie · 2012 to 2026
$10.3M
Mechanistic-Based Non-Invasive Assessment of Retinal Damage in HAART EraR01EY016323 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BARTSCH, DIRK-UWE G · 2006 to 2020
$5.0M
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 P30 EY022589NEI NIH HHS R01 EY016323NEI NIH HHS R01 EY033847
6 · The paper itself

Abstract

Optical Coherence Tomography (OCT) is a powerful technique for non-invasive 3D imaging of biological tissues at high resolution that has revolutionized retinal imaging. A major challenge in OCT imaging is the motion artifacts introduced by involuntary eye movements. In this paper, we propose a convolutional neural network that learns to correct axial motion in OCT based on a single volumetric scan. The proposed method is able to correct large motion, while preserving the overall curvature of the retina. The experimental results show significant improvements in visual quality as well as overall error compared to the conventional methods in both normal and disease cases.

Indexed as

deep learningeye movementMotion correctionoptical coherence tomographyretinal imaging

Identifiers

PMID35950046
PMCPMC9359411

What OpenQuestion holds

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Registered trials

None linked

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.