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Classification of Cross-sections for Vascular Skeleton Extraction Using Convolutional Neural Networks

  • Kristína LidayováEmail author
  • Anindya Gupta
  • Hans Frimmel
  • Ida-Maria Sintorn
  • Ewert Bengtsson
  • Örjan Smedby
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 723)

Abstract

Recent advances in Computed Tomography Angiography provide high-resolution 3D images of the vessels. However, there is an inevitable requisite for automated and fast methods to process the increased amount of generated data. In this work, we propose a fast method for vascular skeleton extraction which can be combined with a segmentation algorithm to accelerate the vessel delineation. The algorithm detects central voxels - nodes - of potential vessel regions in the orthogonal CT slices and uses a convolutional neural network (CNN) to identify the true vessel nodes. The nodes are gradually linked together to generate an approximate vascular skeleton. The CNN classifier yields a precision of 0.81 and recall of 0.83 for the medium size vessels and produces a qualitatively evaluated enhanced representation of vascular skeletons.

Keywords

Vascular skeleton CT angiography Convolutional neural networks Classification 

Notes

Acknowledgements

Lidayová, Frimmel, Bengtsson, and Smedby have been supported by the Swedish Research Council (VR), grant no. 621-2014-6153. Gupta has been supported by Skype IT Academy Stipend Program, EU institutional grant IUT19-11 of Estonian Research Council.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Kristína Lidayová
    • 1
    Email author
  • Anindya Gupta
    • 2
  • Hans Frimmel
    • 3
  • Ida-Maria Sintorn
    • 1
  • Ewert Bengtsson
    • 1
  • Örjan Smedby
    • 4
  1. 1.Department of IT, Centre for Image AnalysisUppsala UniversityUppsalaSweden
  2. 2.T. J. Seebeck Department of ElectronicsTallinn University of TechnologyTallinnEstonia
  3. 3.Division of Scientific Computing, Department of ITUppsala UniversityUppsalaSweden
  4. 4.School of Technology and HealthKTH Royal Institute of TechnologyStockholmSweden

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