Comparative Study of Automated Algorithms for Brain Arteriovenous Malformation Nidus Extent Identification Using 3DRA.
AVM
Angiography
Nidus identification
Vascular-interventional
Journal
Cardiovascular engineering and technology
ISSN: 1869-4098
Titre abrégé: Cardiovasc Eng Technol
Pays: United States
ID NLM: 101531846
Informations de publication
Date de publication:
12 2023
12 2023
Historique:
received:
22
06
2023
accepted:
18
09
2023
medline:
22
12
2023
pubmed:
3
10
2023
entrez:
2
10
2023
Statut:
ppublish
Résumé
When performing a brain arteriovenous malformation (bAVMs) intervention, computer-assisted analysis of bAVMs can aid clinicians in planning precise therapeutic alternatives. Therefore, we aim to assess currently available methods for bAVMs nidus extent identification over 3DRA. To this end, we establish a unified framework to contrast them over the same dataset, fully automatising the workflows. We retrospectively collected contrast-enhanced 3DRA scans of patients with bAVMs. A segmentation network was used to automatically acquire the brain vessels segmentation for each case. We applied the nidus extent identification algorithms over each of the segmentations, computing overlap measurements against manual nidus delineations. We evaluated the methods over a private dataset with 22 3DRA scans of individuals with bAVMs. The best-performing alternatives resulted in [Formula: see text] and [Formula: see text] dice coefficient values. The mathematical morphology-based approach showed higher robustness through inter-case variability. The skeleton-based approach leverages the skeleton topomorphology characteristics, while being highly sensitive to anatomical variations and the skeletonisation method employed. Overall, nidus extent identification algorithms are also limited by the quality of the raw volume, as the consequent imprecise vessel segmentation will hinder their results. Performance of the available alternatives remains subpar. This analysis allows for a better understanding of the current limitations.
Identifiants
pubmed: 37783951
doi: 10.1007/s13239-023-00688-w
pii: 10.1007/s13239-023-00688-w
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
801-809Subventions
Organisme : FONCYT - ANPCYT
ID : PICT 2021-0023
Organisme : FONCYT - ANPCYT
ID : PICT 2020-0045
Organisme : CONICET
ID : PIP 2021-2023 11220200102472CO
Informations de copyright
© 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
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