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A GPU Based Diffusion Method for Whole-Heart and Great Vessel Segmentation

  • Philipp LöselEmail author
  • Vincent Heuveline
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10129)

Abstract

Segmenting the blood pool and myocardium from a 3D cardiovascular magnetic resonance (CMR) image allows to create a patient-specific heart model for surgical planning in children with complex congenital heart disease (CHD). Implementation of semi-automatic or automatic segmentation algorithms is challenging because of a high anatomical variability of the heart defects, low contrast, and intensity variations in the images. Therefore, manual segmentation is the gold standard but it is labor-intensive. In this paper we report the set-up and results of a highly scalable semi-automatic diffusion algorithm for image segmentation. The method extrapolates the information from a small number of expert manually labeled reference slices to the remaining volume. While results of most semi-automatic algorithms strongly depend on well-chosen but usually unknown parameters this approach is parameter-free. Validation is performed on twenty 3D CMR images.

Keywords

Segmentation Diffusion Random walks Interactive segmentation Semi-automatic segmentation Multi-GPU 

Notes

Acknowledgements

This work was carried out with the support of the Federal Ministry of Education and Research (BMBF), Germany, within the collaboration center ASTOR (Arthropod Structure revealed by ultra-fast Tomography and Online Reconstruction) and NOVA (Network for Online Visualization and synergistic Analysis of Tomographic Data).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR)Heidelberg UniversityHeidelbergGermany

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