Evidence map›Paper›PMID 41970584›Full record

ArticleBiomedical optics express2026

Semi-supervised 3D diabetic macular edema segmentation in OCT volumes via independent dual-branch consistency learning.

Zitong Liu, Kaiming Li, Shuaichen Lin, Mohan Qin, Renxiong Wu, Yunchun Zou, Jie Zhong, Yong Liu, Guangming Ni

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomedical optics express, 2026. 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. 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

9 authors.

Zitong LiuSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Kaiming LiDepartment of Ophthalmology, Suining Central Hospital, Suining 629000, China.
Shuaichen LinSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Mohan QinSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Renxiong WuSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Yunchun ZouSchool of Optometry & Ophthalmology, North Sichuan Medical College, NanChong, Sichuan 637002, China.
Jie ZhongSchool of Medicine, University of Electronic Science and Technology of China, Chengdu 610054, China.
Yong LiuSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Guangming NiSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.ORCID https://orcid.org/0000-0002-4516-256X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate 3D lesion segmentation in optical coherence tomography (OCT) images is crucial for the detection and treatment evaluation of diabetic macular edema (DME), but is hindered by scarce labeled data and complex boundaries. To address these challenges, we propose a semi-supervised 3D DME segmentation method that leverages a dual independent-branch co-training framework with mask perturbation consistency. The dual branches jointly exploit complementary multi-view features, while an independence-measuring strategy encourages parameter divergence to break performance bottlenecks. To improve robustness against diverse lesion morphologies, we introduce dynamic region-mask augmentation and a bidirectional confidence filtering mechanism, enforcing strong consistency constraints between the two branches. Additionally, a lightweight channel-attention module integrates multi-scale context. Extensive experiments demonstrate that our method surpasses state-of-the-art approaches, validating its effectiveness and superiority.

Identifiers

PMID41970584
PMCPMC13064604

What OpenQuestion holds

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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.