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Filling Large Discontinuities in 3D Vascular Networks Using Skeleton- and Intensity-Based Information

  • Russell Bates
  • Laurent Risser
  • Benjamin Irving
  • Bartłomiej W. Papież
  • Pavitra Kannan
  • Veerle Kersemans
  • Julia A. Schnabel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9351)

Abstract

Segmentation of vasculature is a common task in many areas of medical imaging, but complex morphology and weak signal often lead to incomplete segmentations. In this paper, we present a new gap filling strategy for 3D vascular networks. The novelty of our approach is to combine both skeleton- and intensity-based information to fill large discontinuities. Our approach also does not make any hypothesis on the network topology, which is particularly important for tumour vasculature due to the chaotic arrangement of vessels within tumours. Synthetic results show that using intensity-based information, in addition to skeleton-based information, can make the detection of large discontinuities more robust. Our strategy is also shown to outperform a classic gap filling strategy on 3D Micro-CT images of preclinical tumour models.

Keywords

Tumour Vasculature Path Search Intensity Information Tensor Model Medical Image Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Russell Bates
    • 1
  • Laurent Risser
    • 2
  • Benjamin Irving
    • 1
  • Bartłomiej W. Papież
    • 1
  • Pavitra Kannan
    • 3
  • Veerle Kersemans
    • 3
  • Julia A. Schnabel
    • 1
  1. 1.Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordOxfordUK
  2. 2.Institut de Mathématiques de Toulouse (UMR 5219), CNRSParisFrance
  3. 3.Department of OncologyUniversity of OxfordOxfordUK

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