Evidence map›Paper›PMID 41667492›Full record

ArticleScientific data2026

Comprehensive 3D Optical Coherence Tomography Dataset for AMD and DME: Facilitating Deep-Learning-Based 3D Segmentation.

Wenjing Huang, Lang Qin, Mingxin Xu, Hao Zheng, Yuxing Gan, Shaoyu Pei, Renxiong Wu, Yong Liu, Jie Zhong, Guangming Ni

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PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 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

10 authors.

Wenjing Huang *School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Lang Qin *School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Mingxin Xu *Eye School of Chengdu University of Traditional Chinese Medicine, Chengdu, 610032, China.
Hao ZhengSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Yuxing GanSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Shaoyu PeiSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Renxiong WuSchool of Optoelectronic Science and Engineering, 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, 610054, China.
Jie ZhongDepartment of Ophthalmology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China.
Guangming NiSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China. guangmingni@uestc.edu.cn.

Funding

China Postdoctoral Science Foundation 2021T140090, 2019M663465National Natural Science Foundation of China (National Science Foundation of China) 61905036
6 · The paper itself

Abstract

Age-related macular degeneration (AMD) and diabetic macular edema (DME) are vision-threatening pathologies for which optical coherence tomography (OCT) provides high-resolution three-dimensional imaging, facilitating comprehensive diagnostic evaluation. Three-dimensional (3D) visualization and precise 3D segmentation of lesions enable accurate assessment of morphology, dimensions, and spatial relationships, thereby enhancing clinical analysis and disease management. However, the lack of a 3D dataset for AMD and DME significantly limits the reliability, robustness, and applicability of deep learning-based 3D segmentation techniques in this field. Here, we present an OCT dataset comprising 224 volumetric images, including 122 for AMD and 102 for DME, annotated with pigment epithelial detachment and intraretinal fluid. We propose a novel 3D segmentation network based on the BiFormer Block, which employs Bi-Level Routing Attention to capture local context and long-range dependencies. Experiments demonstrated that the proposed dataset and network will facilitate the exploration and validation of novel 3D segmentation methods for AMD and DME.

Indexed as

Deep LearningDiabetic RetinopathyImaging, Three-DimensionalMacular DegenerationMacular EdemaTomography, Optical CoherenceHumans

Identifiers

PMID41667492
PMCPMC12894698

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.