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Simultaneous Intracranial Artery Tracing and Segmentation from Magnetic Resonance Angiography by Joint Optimization from Multiplanar Reformation

  • Li Chen
  • Gaoang Wang
  • Niranjan Balu
  • Mahmud Mossa-Basha
  • Xihai Zhao
  • Rui Li
  • Le He
  • Thomas S. Hatsukami
  • Jenq-Neng Hwang
  • Chun YuanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11794)

Abstract

Time-of-flight (TOF) Magnetic Resonance Angiography (MRA) is a useful imaging technique which reflects blood flow and vasculature information. However, due to the low signal and contrast of arteries in TOF MRA, it is challenging to extract vascular features such as length, volume and tortuosity, through segmentation and tracing. Hence, in this paper, a simultaneous artery tracing and segmentation method is proposed to a generate quantitative intracranial vasculature map from TOF MRA. Instead of using original images, segmentation from a neural network model is used to initiate tracing, avoiding the low signal or contrast for small arteries. A tracing method is proposed based on cross-sectional best matching, followed by an optimization scheme from the multiplanar reformatted view. Centerline positions, lumen radii and centerline deviations are jointly optimized for robust tracing within artery regions. Finally, the refined artery traces are used for better artery segmentation. The method is validated on eight TOF MRAs of both healthy subjects and patients with cerebrovascular disease, showing good agreements with human supervised tracing and segmentation results for representative features such as artery length (<4% mean absolute difference), volume (>0.80 Dice), and tortuosity (<3% mean absolute difference). Our method out-performs three other popular tracing and segmentation methods by a large margin.

Keywords

Artery tracing Artery segmentation Magnetic Resonance Angiography Optimization Multiplanar reformation 

Supplementary material

490874_1_En_24_MOESM1_ESM.pdf (798 kb)
Supplementary material 1 (PDF 797 kb)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Li Chen
    • 1
  • Gaoang Wang
    • 1
  • Niranjan Balu
    • 1
  • Mahmud Mossa-Basha
    • 1
  • Xihai Zhao
    • 2
  • Rui Li
    • 2
  • Le He
    • 2
  • Thomas S. Hatsukami
    • 1
  • Jenq-Neng Hwang
    • 1
  • Chun Yuan
    • 1
    Email author
  1. 1.University of WashingtonSeattleUSA
  2. 2.Tsinghua UniversityBeijingChina

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