Automatic lumen and outer wall segmentation of the carotid artery using deformable three-dimensional models in MR angiography and vessel wall images

Authors

  • Ronald van 't Klooster MSc,

    1. Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
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  • Patrick J.H. de Koning MSc,

    1. Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
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  • Reza Alizadeh Dehnavi MD, PhD,

    1. Section of Vascular Medicine, Department of General Internal Medicine and Endocrinology, Leiden University Medical Center, Leiden, Netherlands
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  • Jouke T. Tamsma MD, PhD,

    1. Section of Vascular Medicine, Department of General Internal Medicine and Endocrinology, Leiden University Medical Center, Leiden, Netherlands
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  • Albert de Roos MD, PhD,

    1. Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
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  • Johan H.C. Reiber PhD,

    1. Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
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  • Rob J. van der Geest PhD

    Corresponding author
    1. Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
    • Division of Image Processing, Department of Radiology, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands
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Abstract

Purpose:

To develop and validate an automated segmentation technique for the detection of the lumen and outer wall boundaries in MR vessel wall studies of the common carotid artery.

Materials and Methods:

A new segmentation method was developed using a three-dimensional (3D) deformable vessel model requiring only one single user interaction by combining 3D MR angiography (MRA) and 2D vessel wall images. This vessel model is a 3D cylindrical Non-Uniform Rational B-Spline (NURBS) surface which can be deformed to fit the underlying image data. Image data of 45 subjects was used to validate the method by comparing manual and automatic segmentations. Vessel wall thickness and volume measurements obtained by both methods were compared.

Results:

Substantial agreement was observed between manual and automatic segmentation; over 85% of the vessel wall contours were segmented successfully. The interclass correlation was 0.690 for the vessel wall thickness and 0.793 for the vessel wall volume. Compared with manual image analysis, the automated method demonstrated improved interobserver agreement and inter-scan reproducibility. Additionally, the proposed automated image analysis approach was substantially faster.

Conclusion:

This new automated method can reduce analysis time and enhance reproducibility of the quantification of vessel wall dimensions in clinical studies. J. Magn. Reson. Imaging 2012;35:156-165. © 2011 Wiley Periodicals, Inc.

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