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VeNet: a lightweight neural network for efficient brain vessel segmentation in endovascular robotic surgery.

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Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 9 lines · 195 B · no license

  1. from .vnet import VNET
  2. from .unet import UNET
  3. from .fcn import FCN
  4. from .net import Network
  5. from . import layers
  6. from . import metrics
  7. from . import losses
  8. from . import misc
  9. from . import utils

__init__.py at commit 93dbe34, no license · at the source

Overview

Authors: Alexandra Bernadotte1
ORCID iDs: Alexandra Bernadotte
  1. Neurosputnik LLC, Moscow, 101000 Russia
Journal: Scientific reports, volume 16, issue 1, article 24566
Dates: received 24 January 2026; accepted 18 May 2026; published online 29 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-54176-x · PMID 42215575 · PMCID PMC13454300 · OpenAlex W7162790366
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism)
Methods: Connectivity, Machine learning
Keywords: Hessian, Vascular surgery, MRI, AI, Robotic surgery, Computer vision, Pattern recognition, Brain vessels simulation, Dataset, Semi-automatic annotation, 3D segmentation, Neural network, Hessian matrix, State-of-the-Art, SOTA VeNet, Computational biology and bioinformatics, Engineering, Health care, Mathematics and computing, Medical research
MeSH: Brain*, Endovascular Procedures*, Neural Networks, Computer*, Robotic Surgical Procedures*, Algorithms, Humans, Imaging, Three-Dimensional (* major topic)
Topic: Retinal Imaging and Analysis (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Citations: not cited yet (Europe PMC); 45 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.

Repositories

Its files are read in the Code ↔ Paper reader above.

git.scinalytics.com/rebis/vessel-segmentator

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source: git.scinalytics.com/Rebis/vessel-segmentator

giesekow/deepvesselnet

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 93dbe3436764da0fc6f7ad7f78cda5a8174728ed, 7 July 2023
Languages: Python (12)
Size: 15 files, 12 scripts
Software Heritage: archived
Found in: the references
Holds: README, environment (requirements.txt, setup.py)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (9 files), Keras (7 files), TensorFlow (2 files), Matplotlib (1 file), scikit-learn (1 file), SimpleITK (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
13 files
At the source: github.com/giesekow/deepvesselnet/wiki/Datasets

Code availability statement

The paper has a code 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/s41598-026-54176-x.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 12 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

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:

  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41598-026-54176-x.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 1 author, 20 keywords, 7 MeSH terms, 23 references.

Cite

This paper

Bernadotte, A. (2026). VeNet: a lightweight neural network for efficient brain vessel segmentation in endovascular robotic surgery. Scientific reports, 16(1), 24566. https://doi.org/10.1038/s41598-026-54176-x

BibTeX

@article{bernadotte2026venet,
author = {Bernadotte, Alexandra},
title = {{VeNet: a lightweight neural network for efficient brain vessel segmentation in endovascular robotic surgery}},
journal = {Scientific reports},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {24566},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-54176-x},
url = {https://doi.org/10.1038/s41598-026-54176-x},
pmid = {42215575},
pmcid = {PMC13454300}
}

RIS

TY - JOUR
AU - Bernadotte, Alexandra
TI - VeNet: a lightweight neural network for efficient brain vessel segmentation in endovascular robotic surgery
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/05/29
VL - 16
IS - 1
SP - 24566
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-54176-x
UR - https://doi.org/10.1038/s41598-026-54176-x
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41598-026-54176-x",
"type": "article-journal",
"title": "VeNet: a lightweight neural network for efficient brain vessel segmentation in endovascular robotic surgery",
"container-title": "Scientific reports",
"author": [
{
"family": "Bernadotte",
"given": "Alexandra"
}
],
"container-title-short": "Sci Rep",
"volume": "16",
"issue": "1",
"page": "24566",
"DOI": "10.1038/s41598-026-54176-x",
"PMID": "42215575",
"PMCID": "PMC13454300",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41598-026-54176-x",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
29
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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