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Circle of Willis-Guided Localization for Simultaneous Detection and Classification of Large Vessel Occlusions in Brain CTA.

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches
  1. [1] § Methods › Network Architecture ↔ nndet/arch/heads/classifier.py, lines 295–364 · score 0.68 · cross entropy loss, classification head, bounding box, convolutional, sigmoid, layer
  2. [2] § Methods › Network Architecture ↔ nndet/arch/decoder/base.py, lines 29–104 · score 0.66 · lateral connections, transposed convolutional, decoder, activation, layer, union

Paper

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

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The authors' code

Python · 430 lines · 15 KB · no license · 1 match

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It can be read at the source: nndet/arch/heads/classifier.py.

Overview

Authors: Valeriia Abramova1, Arnau Oliver1, Uma M Lal-Trehan Estrada1, Rachika E Hamadache1, Paola Martínez Arias1, Jordi Freixenet1, Mikel Terceño2, Yolanda Silva2, Xavier Lladó1
  1. Computer Vision and Robotics Institute, University of Girona, Girona, Catalonia Spain
  2. Department of Neurology, Hospital Universitari Dr Josep Trueta - Institut d’Investigació Biomèdica de Girona, Girona, Catalonia Spain
Institutions: Universitat de Girona (Spain); Institut d'Investigació Biomèdica de Girona (Spain); Hospital Universitari de Girona Doctor Josep Trueta (Spain)
Journal: Neuroinformatics, volume 24, issue 4, article 62
Dates: received 16 January 2026; accepted 2 September 2026; published online 16 September 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s12021-026-09817-x · PMID 42747736 · PMCID PMC13582341 · OpenAlex W7213233328
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: other (modality), human (organism)
Methods: Connectivity, Machine learning, fMRI & imaging
Keywords: Computed tomography angiography, Large vessel occlusion, Circle of willis, nnDetection
MeSH: Brain*, Cerebral Angiography*, Circle of Willis*, Computed Tomography Angiography*, Humans (* major topic)
Topic: Acute Ischemic Stroke Management (Epidemiology, Medicine), according to OpenAlex
Funding: Nvidia; Universitat de Girona (IFUdG2024); Ministerio de Ciencia, Innovación y Universidades (PID2023, PID2023-146187OB-I00, PRE2021-099121, DPI2020-114769RB-I00); Fundació Institució Catalana de Recerca i Estudis Avançats (ICREA); Horizon 2020 Framework Programme
Citations: not cited yet (Europe PMC); 32 references in the paper

Abstract

Large vessel occlusions (LVOs) are blockages in the brain’s major arteries that can cause severe neurological damage. Rapid and accurate detection using computed tomography angiography (CTA) is critical for timely stroke treatment. Here, we present a fully automated approach that detects LVOs and classifies the affected vessel simultaneously. Our method incorporates a spatial prior by using Circle of Willis (CoW) segmentation as additional input, guiding the model to anatomically relevant regions. We evaluated the two strategies, the global approach using the full CTA volume, and the local one focused on CoW regions. Both achieved high performance. Detection sensitivity was 0.97 at 0.20 false positives per image for the global approach, and 0.97 at 0.13 false positives for the local approach. Classification accuracy reached 94% and 91% for global and local strategies, respectively. Importantly, the local approach was 3.3× faster, offering a computationally efficient solution, a critical advantage in acute stroke care, where every minute impacts patient outcomes.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

MIC-DKFZ/nnDetection

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 97a58f3110b71caf1b4bcc1851e67cf11e987fc5, 27 October 2025
Languages: Python (161), C++ (2), C/C++ (1), CUDA (1), Shell (1)
Size: 223 files, 166 scripts
Software Heritage: not archived
Found in: “Information Sharing Statement”
Holds: README, license file, environment (Dockerfile, requirements.txt, setup.cfg, setup.py, projects/Task019_ADAM/submission/Dockerfile), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: PyTorch (56 files), NumPy (53 files), SimpleITK (15 files), pandas (6 files), scikit-learn (6 files), PyTorch Lightning (5 files), Matplotlib (5 files), nnU-Net (5 files), SciPy (5 files), scikit-image (2 files), seaborn (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
167 files, not copied: shown from their source

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At the source: github.com/MIC-DKFZ/nnDetection

NIC-VICOROB/CoW-multiclass-segmentation-TopCoW24

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: e47cc66e9211d3dab8400ffac599c3523d0f08d9, 3 February 2026
Languages: Python (552), Shell (16), Jupyter (4)
Size: 705 files, 572 scripts
Software Heritage: not archived
Found in: “Information Sharing Statement”
Holds: README, environment (nnUNet/pyproject.toml, nnUNet/setup.py, algo_submission/task-1-seg/Dockerfile, algo_submission/task-1-seg/requirements.txt, algo_submission/task-2-box/Dockerfile, algo_submission/task-2-box/requirements.txt), tests, documentation, 4 notebooks
Not found: license file, CITATION.cff, continuous integration
Tools: nnU-Net (312 files), NumPy (221 files), PyTorch (166 files), SimpleITK (25 files), scikit-image (20 files), SciPy (18 files), NiBabel (12 files), Matplotlib (10 files), pandas (10 files), tifffile (6 files), seaborn (4 files), scikit-learn (3 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
573 files, not copied: shown from their source

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At the source: github.com/NIC-VICOROB/CoW-multiclass-segmentation-TopCoW24

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;
  • 738 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • 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

No dataset and no data link were found in the paper.

Data Availability

The in-house dataset used for developing of the algorithm in the current study is not publicly available due to the confidentiality policy and institutional patient privacy regulation. The CODEC-IV dataset can be obtained in accordance with the original data provider’s and license terms.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 3, 28 September 2026

  • Publisher: — → Springer Science+Business Media
  • Funding: added Nvidia; Universitat de Girona: IFUdG2024; Ministerio de Ciencia, Innovación y Universidades: PID2023, PID2023-146187OB-I00, PRE2021-099121, DPI2020-114769RB-I00; Institució Catalana de Recerca i Estudis Avançats; Education, Audiovisual and Culture Executive Agency

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 4 keywords, 5 MeSH terms, 30 references.

Cite

This paper

Abramova, V., Oliver, A., Lal-Trehan Estrada, U. M., Hamadache, R. E., Martínez Arias, P., Freixenet, J., Terceño, M., Silva, Y., & Lladó, X. (2026). Circle of Willis-Guided Localization for Simultaneous Detection and Classification of Large Vessel Occlusions in Brain CTA. Neuroinformatics, 24(4), 62. https://doi.org/10.1007/s12021-026-09817-x

BibTeX

@article{abramova2026circle,
author = {Abramova, Valeriia and Oliver, Arnau and Lal-Trehan Estrada, Uma M and Hamadache, Rachika E and Martínez Arias, Paola and Freixenet, Jordi and Terceño, Mikel and Silva, Yolanda and Lladó, Xavier},
title = {{Circle of Willis-Guided Localization for Simultaneous Detection and Classification of Large Vessel Occlusions in Brain CTA}},
journal = {Neuroinformatics},
year = {2026},
month = sep,
volume = {24},
number = {4},
pages = {62},
publisher = {Springer Science+Business Media},
issn = {1539-2791},
doi = {10.1007/s12021-026-09817-x},
url = {https://doi.org/10.1007/s12021-026-09817-x},
pmid = {42747736},
pmcid = {PMC13582341}
}

RIS

TY - JOUR
AU - Abramova, Valeriia
AU - Oliver, Arnau
AU - Lal-Trehan Estrada, Uma M
AU - Hamadache, Rachika E
AU - Martínez Arias, Paola
AU - Freixenet, Jordi
AU - Terceño, Mikel
AU - Silva, Yolanda
AU - Lladó, Xavier
TI - Circle of Willis-Guided Localization for Simultaneous Detection and Classification of Large Vessel Occlusions in Brain CTA
T2 - Neuroinformatics
J2 - Neuroinformatics
PY - 2026
DA - 2026/09/16
VL - 24
IS - 4
SP - 62
SN - 1539-2791
PB - Springer Science+Business Media
DO - 10.1007/s12021-026-09817-x
UR - https://doi.org/10.1007/s12021-026-09817-x
LA - en
ER -

CSL-JSON

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

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