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Learning-based automated segmentation of the carotid artery vessel wall in dual-sequence MRI using subdivision surface fitting.

Authors
  • Gao, Shan1
  • van 't Klooster, Ronald1
  • Kitslaar, Pieter H1
  • Coolen, Bram F2
  • van den Berg, Alexandra M2
  • Smits, Loek P2
  • Shahzad, Rahil1
  • Shamonin, Denis P1
  • de Koning, Patrick J H1
  • Nederveen, Aart J2
  • van der Geest, Rob J1
  • 1 Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands. , (Netherlands)
  • 2 Department of Radiology, Academic Medical Center, 1100 DD, Amsterdam, The Netherlands. , (Netherlands)
Type
Published Article
Journal
Medical Physics
Publisher
Wiley (John Wiley & Sons)
Publication Date
Oct 01, 2017
Volume
44
Issue
10
Pages
5244–5259
Identifiers
DOI: 10.1002/mp.12476
PMID: 28715090
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

The quantification of vessel wall morphology and plaque burden requires vessel segmentation, which is generally performed by manual delineations. The purpose of our work is to develop and evaluate a new 3D model-based approach for carotid artery wall segmentation from dual-sequence MRI. The proposed method segments the lumen and outer wall surfaces including the bifurcation region by fitting a subdivision surface constructed hierarchical-tree model to the image data. In particular, a hybrid segmentation which combines deformable model fitting with boundary classification was applied to extract the lumen surface. The 3D model ensures the correct shape and topology of the carotid artery, while the boundary classification uses combined image information of 3D TOF-MRA and 3D BB-MRI to promote accurate delineation of the lumen boundaries. The proposed algorithm was validated on 25 subjects (48 arteries) including both healthy volunteers and atherosclerotic patients with 30% to 70% carotid stenosis. For both lumen and outer wall border detection, our result shows good agreement between manually and automatically determined contours, with contour-to-contour distance less than 1 pixel as well as Dice overlap greater than 0.87 at all different carotid artery sections. The presented 3D segmentation technique has demonstrated the capability of providing vessel wall delineation for 3D carotid MRI data with high accuracy and limited user interaction. This brings benefits to large-scale patient studies for assessing the effect of pharmacological treatment of atherosclerosis by reducing image analysis time and bias between human observers. © 2017 American Association of Physicists in Medicine.

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