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

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Reconstruction, Segmentation, and Analysis of Medical Images (RAMBO 2016, HVSMR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,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.

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Notes

  1. 1.

    4 Nvidia Tesla K40, 4 Nvidia Tesla K20, 1 Nvidia Grid K2, 1 Nvidia GeForce GTX 770.

References

  1. Grady, L.: Random walks for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1768–1783 (2006)

    Article  Google Scholar 

  2. Lösel, P., Heuveline, V.: Enhancing a diffusion algorithm for 4D image segmentation using local information. In: Proceedings of SPIE 9784, Medical Imaging 2016: Image Processing, 97842L (2016). doi:10.1117/12.2216202

  3. Pace, D.F., Dalca, A.V., Geva, T., Powell, A.J., Moghari, M.H., Golland, P.: Interactive whole-heart segmentation in congenital heart disease. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 80–88. Springer, Heidelberg (2015). doi:10.1007/978-3-319-24574-4_10

    Chapter  Google Scholar 

  4. Schmauss, D., Haeberle, S., Hagl, C., Sodian, R.: Three-dimensional printing in cardiac surgery and interventional cardiology: a single-centre experience. Eur. J. Cardio.-Thorac. Surg. 47, 1044–1052 (2014)

    Article  Google Scholar 

  5. Valverde, I., Gomez, G., Gonzalez, A., Suarez-Mejias, C., Adsuar, A., Coserria, J.F., Uribe, S., Gomez-Cia, T., Hosseinpour, A.R.: Three-dimensional patient-specific cardiac model for surgical planning in Nikaidoh procedure. Cardiol. Young 25(4), 698–704 (2014)

    Article  Google Scholar 

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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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Authors and Affiliations

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

    Philipp Lösel & Vincent Heuveline

Authors
  1. Philipp Lösel
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  2. Vincent Heuveline
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Corresponding author

Correspondence to Philipp Lösel .

Editor information

Editors and Affiliations

  1. University College London, London, United Kingdom

    Maria A. Zuluaga

  2. King’s College London, London, United Kingdom

    Kanwal Bhatia

  3. Imperial College London, London, United Kingdom

    Bernhard Kainz

  4. Harvard Medical School and Boston Children’s Hospital, Boston, Massachusetts, USA

    Mehdi H. Moghari

  5. MIT, Cambridge, Massachusetts, USA

    Danielle F. Pace

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Lösel, P., Heuveline, V. (2017). A GPU Based Diffusion Method for Whole-Heart and Great Vessel Segmentation. In: Zuluaga, M., Bhatia, K., Kainz, B., Moghari, M., Pace, D. (eds) Reconstruction, Segmentation, and Analysis of Medical Images. RAMBO HVSMR 2016 2016. Lecture Notes in Computer Science(), vol 10129. Springer, Cham. https://doi.org/10.1007/978-3-319-52280-7_12

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  • DOI: https://doi.org/10.1007/978-3-319-52280-7_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52279-1

  • Online ISBN: 978-3-319-52280-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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