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Comparative Study of Automated Algorithms for Brain Arteriovenous Malformation Nidus Extent Identification Using 3DRA

Cardiovascular Engineering and Technology Aims and scope Submit manuscript

Abstract

Purpose

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.

Materials and Methods

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.

Results

We evaluated the methods over a private dataset with 22 3DRA scans of individuals with bAVMs. The best-performing alternatives resulted in \(\mathbf{0.82\pm 0.14}\) and \(\mathbf{0.81\pm 0.16}\) dice coefficient values.

Conclusions

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.

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Funding

CG is funded by a CONICET PhD Scholarship. This study was partially funded by PICT 2021-0023 and PICT 2020-0045—FONCYT—ANPCYT (Argentina), PIP 2021-2023 11220200102472CO from CONICET (Argentina), a NVIDIA Hardware Grant, and partially supported by the computing facilities of Extremadura Research Centre for Advanced Technologies (CETA-CIEMAT) of the Government of Spain, funded by the European Regional Development Fund (ERDF).

Author information

Author notes

  1. Yibin Fang and Ignacio Larrabide are co-last authors.

Authors and Affiliations

  1. Yatiris Group, PLADEMA Institute, UNICEN, Campus Universitario, Tandil, Argentina

    Camila García & Ignacio Larrabide

  2. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tandil, Argentina

    Camila García & Ignacio Larrabide

  3. Department of Neuroradiology, University Hospital of Southampton, Southampton, UK

    Ana Paula Narata

  4. Department of Neurosurgery, Changhai Hospital, Naval Medical University, Shanghai, China

    Jianmin Liu

  5. Department of Neurovascular Disease, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China

    Yibin Fang

Authors
  1. Camila García
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  2. Ana Paula Narata
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  3. Jianmin Liu
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  4. Yibin Fang
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  5. Ignacio Larrabide
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Corresponding author

Correspondence to Camila García.

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Conflict of interest

The authors have no competing interests to declare that are relevant to the content of this article.

Ethical Approval

This research study was conducted retrospectively and data collection was approved by the clinical review board of the Changhai Hospital (Naval Medical University, Shanghai, P. R. China).

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Associate Editor Francesco Migliavacca oversaw the review of this article.

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García, C., Narata, A.P., Liu, J. et al. Comparative Study of Automated Algorithms for Brain Arteriovenous Malformation Nidus Extent Identification Using 3DRA. Cardiovasc Eng Tech 14, 801–809 (2023). https://doi.org/10.1007/s13239-023-00688-w

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  • DOI: https://doi.org/10.1007/s13239-023-00688-w

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