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Computer-aided nidus segmentation and angiographic characterization of arteriovenous malformations

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Exact knowledge about the nidus of an arteriovenous malformation (AVM) and the connected vessels is often required for image-based research projects and optimal therapy planning. The aim of this work is to present and evaluate a computer-aided nidus segmentation technique and subsequent angiographic characterization of the connected vessels that can be visualized in 3D.

Methods

The proposed method was developed and evaluated based on 15 datasets of patients with an AVM. Each dataset consists of a high-resolution 3D and a 4D magnetic resonance angiography (MRA) image sequence. After automatic cerebrovascular segmentation from the 3D MRA dataset, a voxel-wise support vector machine classification based on four extracted features is performed to generate a new parameter map. The nidus is represented by positive values in this parameter map and can be extracted using volume growing. Finally, the nidus segmentation is dilated and used for an automatic identification of feeding arteries and draining veins by integrating hemodynamic information from the 4D MRA datasets.

Results

A quantitative comparison of the computer-aided AVM nidus segmentation results to manual gold-standard segmentations by two observers revealed a mean Dice coefficient of 0.835, which is comparable to the inter-observer agreement for which a mean Dice coefficient of 0.830 was determined. The angiographic characterization was visually rated feasible for all patients.

Conclusion

The presented computer-aided method enables a reproducible and fast extraction of the AVM nidus as well as an automatic angiographic characterization of the connected vessels, which can be used to support image-based research projects and therapy planning of AVMs.

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Authors and Affiliations

  1. Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Bldg. W36, Martinistraße 52, 20246 , Hamburg, Germany

    Nils Daniel Forkert, Till Illies, Einar Goebell & Jens Fiehler

  2. Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Dennis Säring

  3. Institute of Medical Informatics, University of Lübeck, Lübeck, Germany

    Heinz Handels

Authors
  1. Nils Daniel Forkert
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  2. Till Illies
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  3. Einar Goebell
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  4. Jens Fiehler
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  5. Dennis Säring
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  6. Heinz Handels
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Correspondence to Nils Daniel Forkert.

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Forkert, N.D., Illies, T., Goebell, E. et al. Computer-aided nidus segmentation and angiographic characterization of arteriovenous malformations. Int J CARS 8, 775–786 (2013). https://doi.org/10.1007/s11548-013-0823-9

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  • DOI: https://doi.org/10.1007/s11548-013-0823-9

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