Evidence map›Paper›PMID 42434689›Full record

ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2026

Unsupervised OCT Image Interpolation Using Deformable Registration and generative models.

Shuwen Wei, Samuel W Remedios, Zhangxing Bian, Shimeng Wang, Junyu Chen, Yihao Liu, Bruno Jedynak, Tin Y A Liu, Shiv Saidha, Peter A Calabresi and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2026. 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. Optical Coherence Tomography Harmonization via Dual Diffusion Implicit Bridges.Proceedings of SPIE--the International Society for Optical Engineering · 2026
    Article
  2. 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

12 authors.

Shuwen WeiImage Analysis and Communications Laboratory, Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0001-8679-9615
Samuel W RemediosImage Analysis and Communications Laboratory, Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0001-8634-8128
Zhangxing BianImage Analysis and Communications Laboratory, Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0003-3603-0650
Shimeng WangImage Analysis and Communications Laboratory, Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0009-0006-5096-2664
Junyu ChenImage Analysis and Communications Laboratory, Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0003-4672-6408
Yihao LiuDepartment of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, USA.ORCID 0000-0003-3187-9903
Bruno JedynakDepartment of Mathematics and Statistics, Portland State University, Portland, OR 97201, USA.ORCID 0000-0002-0824-5745
Tin Y A LiuDepartment of Ophthalmology, Wilmer Eye Institute, Baltimore, MD 21287, USA.ORCID 0000-0003-2957-0755
Shiv SaidhaDepartment of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA.
Peter A CalabresiDepartment of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA.ORCID 0000-0002-7776-6472
Jerry L PrinceImage Analysis and Communications Laboratory, Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0002-6553-0876
Aaron CarassImage Analysis and Communications Laboratory, Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0003-4939-5085

Funding

Wisconsin Registry for Alzheimer's Prevention: Sex Differences in DNA MethylationR01AG027161 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI Nathaniel Ark Chin, Sterling C Johnson · 2007 to 2026
$35.6M
The Longitudinal Course of Neural Function and Amyloid in People At Risk for ADR01AG021155 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI Sterling C Johnson · 2004 to 2026
$27.0M
Imaging Neurodegeneration in Multiple SclerosisR01NS082347 · NINDS · JOHNS HOPKINS UNIVERSITY · PI PETER A CALABRESI, Shiv Saidha · 2013 to 2026
$7.0M
OCT and OCTA image processing for retinal assessment of people with MSR01EY032284 · NEI · JOHNS HOPKINS UNIVERSITY · PI PRINCE, JERRY L · 2021 to 2024
$1.8M
NEI NIH HHS R01 EY032284NIA NIH HHS R01 AG021155NIA NIH HHS R01 AG027161NINDS NIH HHS R01 NS082347
6 · The paper itself

Abstract

Optical coherence tomography (OCT) images are often acquired as highly anisotropic volumes, where the scanning step is dense along the fast axis but sparse along the slow axis. This affects image analysis, such as image registration for longitudinal alignment. To create more isotropic volumes, bicubic interpolation can be used along the slow axis, but it generally produces blurry features. Registration-based interpolation can reduce blurriness, but often fails to generate realistic OCT images. Deep generative models can sample realistic images, but lack the structural consistency constraints required for interpolation. In this paper, we propose an unsupervised image interpolation method that combines registration-based interpolation with a deep generative model to overcome their individual limitations and improve the structural accuracy and realism of interpolated OCT images. We compare the proposed method with both bicubic and registration-based interpolation on real OCT datasets, and show that it achieves the best interpolation performance.

Indexed as

Deformable registrationGenerative modelImage interpolationOptical coherence tomographyUnsupervised learning

Identifiers

PMID42434689
PMCPMC13352931

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.