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nnLoGoNet: a hybrid local-global network for retinal vessel segmentation with Skeleton Recall Loss.

Overview

Authors: Haixia Bai1,2, Fuquan Wu3, Yushuai Zhou4, Shijing Wu1,2, Ailing Sui1,2, Zizhao Wu4, Zhiqing Chen1,2
  1. Eye Center of Second Affi liated Hospital, School of Medicine, Zhejiang University,Hangzhou, Zhejiang China
  2. Zhejiang Provincial Key Laboratory of Ophthalmology,Hangzhou, Zhejiang China
  3. The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine),Hangzhou, Zhejiang China
  4. School of Digital Media Technology, Hangzhou Dianzi University,Hangzhou, China
Journal: Scientific reports, volume 16, issue 1, article 19297
Dates: received 11 February 2026; accepted 17 April 2026; published online 27 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-50020-4 · PMID 42045476 · PMCID PMC13284357 · OpenAlex W7155991829
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: human (organism), methods / tools (subfield)
Methods: Connectivity, Machine learning
Keywords: Computational biology and bioinformatics, Engineering, Mathematics and computing
MeSH: Image Processing, Computer-Assisted*, Retinal Vessels*, Algorithms, Convolutional Neural Networks, Humans, Neural Networks, Computer, Tomography, Optical Coherence (* major topic)
Topic: Retinal Imaging and Analysis (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Citations: not cited yet (Europe PMC); 48 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

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Data

Datasets cited

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41598-026-50020-4.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 3 keywords, 7 MeSH terms, 1 funder, 28 references.

Cite

This paper

Bai, H., Wu, F., Zhou, Y., Wu, S., Sui, A., Wu, Z., & Chen, Z. (2026). nnLoGoNet: a hybrid local-global network for retinal vessel segmentation with Skeleton Recall Loss. Scientific reports, 16(1), 19297. https://doi.org/10.1038/s41598-026-50020-4

BibTeX

@article{bai2026nnlogonet,
author = {Bai, Haixia and Wu, Fuquan and Zhou, Yushuai and Wu, Shijing and Sui, Ailing and Wu, Zizhao and Chen, Zhiqing},
title = {{nnLoGoNet: a hybrid local-global network for retinal vessel segmentation with Skeleton Recall Loss}},
journal = {Scientific reports},
year = {2026},
month = apr,
volume = {16},
number = {1},
pages = {19297},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-50020-4},
url = {https://doi.org/10.1038/s41598-026-50020-4},
pmid = {42045476},
pmcid = {PMC13284357}
}

RIS

TY - JOUR
AU - Bai, Haixia
AU - Wu, Fuquan
AU - Zhou, Yushuai
AU - Wu, Shijing
AU - Sui, Ailing
AU - Wu, Zizhao
AU - Chen, Zhiqing
TI - nnLoGoNet: a hybrid local-global network for retinal vessel segmentation with Skeleton Recall Loss
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/04/27
VL - 16
IS - 1
SP - 19297
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-50020-4
UR - https://doi.org/10.1038/s41598-026-50020-4
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "nnLoGoNet: a hybrid local-global network for retinal vessel segmentation with Skeleton Recall Loss",
"container-title": "Scientific reports",
"author": [
{
"family": "Bai",
"given": "Haixia"
},
{
"family": "Wu",
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{
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"given": "Yushuai"
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{
"family": "Wu",
"given": "Shijing"
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{
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{
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{
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"given": "Zhiqing"
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],
"container-title-short": "Sci Rep",
"volume": "16",
"issue": "1",
"page": "19297",
"DOI": "10.1038/s41598-026-50020-4",
"PMID": "42045476",
"PMCID": "PMC13284357",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41598-026-50020-4",
"language": "en",
"issued": {
"date-parts": [
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2026,
4,
27
]
]
}
}

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