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Elaboration of a semi-automated algorithm for brain arteriovenous malformation segmentation: initial results

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Abstract

Objectives

The purpose of our study was to distinguish the different components of a brain arteriovenous malformation (bAVM) on 3D rotational angiography (3D-RA) using a semi-automated segmentation algorithm.

Materials and methods

Data from 3D-RA of 15 patients (8 males, 7 females; 14 supratentorial bAVMs, 1 infratentorial) were used to test the algorithm. Segmentation was performed in two steps: (1) nidus segmentation from propagation (vertical then horizontal) of tagging on the reference slice (i.e., the slice on which the nidus had the biggest surface); (2) contiguity propagation (based on density and variance) from tagging of arteries and veins distant from the nidus. Segmentation quality was evaluated by comparison with six frame/s DSA by two independent reviewers. Analysis of supraselective microcatheterisation was performed to dispel discrepancy.

Results

Mean duration for bAVM segmentation was 64 ± 26 min. Quality of segmentation was evaluated as good or fair in 93 % of cases. Segmentation had better results than six frame/s DSA for the depiction of a focal ectasia on the main draining vein and for the evaluation of the venous drainage pattern.

Conclusion

This segmentation algorithm is a promising tool that may help improve the understanding of bAVM angio-architecture, especially the venous drainage.

Key points

• The segmentation algorithm allows for the distinction of the AVM’s components

• This algorithm helps to see the venous drainage of bAVMs more precisely

• This algorithm may help to reduce the treatment-related complication rate

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Abbreviations

3D-RA:

Three dimensional-rotational angiography

bAVM:

Brain arteriovenous malformation

DSA:

Digital subtraction angiography

TOF:

Time of flight

TR MRA:

Time-resolved magnetic resonance angiography

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Acknowledgements

The scientific guarantor of this publication is Prof. Charbel Mounayer. The authors of this manuscript declare relationships with the following companies:

Dr Nader Sourour is proctor for the Covidien Company. The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. The authors state that this work has received funding from the AVRUL (Agence pour la Valorisation de la Recherche Universitaire du Limousin), Limoges. FRANCE. One of the authors has significant statistical expertise. Institutional Review Board approval was obtained. Written informed consent was waived by the Institutional Review Board. Methodology: retrospective, experimental, multicentre study.

Author information

Correspondence to Frédéric Clarençon.

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Clarençon, F., Maizeroi-Eugène, F., Bresson, D. et al. Elaboration of a semi-automated algorithm for brain arteriovenous malformation segmentation: initial results. Eur Radiol 25, 436–443 (2015). https://doi.org/10.1007/s00330-014-3421-5

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Keywords

  • Arteriovenous malformation
  • Algorithm
  • Segmentation
  • Digital subtraction angiography
  • 3D imaging