Evidence map›Paper›PMID 38686400›Full record

ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2024

[Brain magnetic resonance image registration based on parallel lightweight convolution and multi-scale fusion].

Yu Shen, Yuan Yan, Jing Song, Guanghui Liu, Jiawen Xu, Ziyi Wei

Abstract readEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2024. 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.

  1. [Cardiac magnetic resonance image segmentation based on lightweight network and knowledge distillation strategy].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024
    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

6 authors.

Yu ShenSchool of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, P. R. China.
Yuan YanSchool of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, P. R. China.
Jing SongSchool of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, P. R. China.
Guanghui LiuSchool of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, P. R. China.
Jiawen XuSchool of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, P. R. China.
Ziyi WeiSchool of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medical image registration plays an important role in medical diagnosis and treatment planning. However, the current registration methods based on deep learning still face some challenges, such as insufficient ability to extract global information, large number of network model parameters, slow reasoning speed and so on. Therefore, this paper proposed a new model LCU-Net, which used parallel lightweight convolution to improve the ability of global information extraction. The problem of large number of network parameters and slow inference speed was solved by multi-scale fusion. The experimental results showed that the Dice coefficient of LCU-Net reached 0.823, the Hausdorff distance was 1.258, and the number of network parameters was reduced by about one quarter compared with that before multi-scale fusion. The proposed algorithm shows remarkable advantages in medical image registration tasks, and it not only surpasses the existing comparison algorithms in performance, but also has excellent generalization performance and wide application prospects.

Indexed as

AlgorithmsBrainImage Processing, Computer-AssistedMagnetic Resonance ImagingDeep LearningHumansNeural Networks, ComputerLightweight modelMedical image processingMulti-scale fusionParallel path

Identifiers

PMID38686400
PMCPMC11058484

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.