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Automated Colorectal Tumour Segmentation in DCE-MRI Using Supervoxel Neighbourhood Contrast Characteristics

  • Benjamin Irving
  • Amalia Cifor
  • Bartłomiej W. Papież
  • Jamie Franklin
  • Ewan M. Anderson
  • Sir Michael Brady
  • Julia A. Schnabel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a powerful protocol for assessing tumour progression from changes in tissue contrast enhancement. Manual colorectal tumour delineation is a challenging and time consuming task due to the complex enhancement patterns in the 4D sequence. There is a need for a consistent approach to colorectal tumour segmentation in DCE-MRI and we propose a novel method based on detection of the tumour from signal enhancement characteristics of homogeneous tumour subregions and their neighbourhoods. Our method successfully detected 20 of 23 cases with a mean Dice score of 0.68 ±0.15 compared to expert annotations, which is not significantly different from expert inter-rater variability of 0.73 ±0.13 and 0.77 ±0.10. In comparison, a standard DCE-MRI tumour segmentation technique, fuzzy c-means, obtained a Dice score of 0.28 ±0.17.

Keywords

Linear Discriminant Analysis Colorectal Tumour Tumour Segmentation Enhancement Curve Expert Annotation 
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 2014

Authors and Affiliations

  • Benjamin Irving
    • 1
  • Amalia Cifor
    • 1
  • Bartłomiej W. Papież
    • 1
  • Jamie Franklin
    • 2
  • Ewan M. Anderson
    • 2
  • Sir Michael Brady
    • 3
  • Julia A. Schnabel
    • 1
  1. 1.Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordUK
  2. 2.Department of RadiologyOxford University Hospitals NHS TrustOxfordUK
  3. 3.Department of OncologyUniversity of OxfordUK

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