Evaluation of Unsupervised Deformable Image Registration using CNN and ViT on 4D-CT

Peizhi Chen, Jialan Wang, Yifan Guo, Yan Wang

Abstract read

In one paragraph

Article in Current medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Peizhi ChenCollege of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China.

Jialan WangCollege of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China.

Yifan GuoCollege of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China.

Yan WangCollege of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionDeformable image registration is essential in medical image analysis. The state-of-the-art approaches are unsupervised methods based on convolutional neural networks (CNN) and vision transformers (ViT). While CNNs perform well in extracting local features, ViTs perform better in extracting global features.

objectiveThis study aimed to compare the performance of CNN and ViT in unsupervised deformable image registration.

methodWe have proposed a unified registration framework and evaluated both architectures. Experiments have been conducted using 4D-CT.

resultsThe results have shown ViT-based registration to achieve superior performance compared to CNN-based methods.

conclusionThe findings have indicated vision transformer architectures to be more effective than convolutional networks for unsupervised deformable registration on 4D-CT data.

Indexed as

Convolutional Neural NetworksFour-Dimensional Computed TomographyImage Processing, Computer-AssistedNeural Networks, ComputerUnsupervised Machine LearningAlgorithmsHumansConvolutional neural networksDeformable image registrationEvaluationMedical image analysisRespiratory motion modelingVision transformerVision transformer.

Identifiers

PMID41133827

DOI10.2174/0115734056385097251010051841

PMCPMC13639908

What OpenQuestion holds

Textmetadata

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.