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Fast Multiatlas Selection Using Composition of Transformations for Radiation Therapy Planning

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Medical Computer Vision: Algorithms for Big Data (MCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8848))

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Abstract

In radiation therapy, multiatlas segmentation is recognized as being accurate, but is generally not considered scalable since the highest accuracy is achieved only when using a large atlas database. The fundamental problem is to use such a large database, to accurately represent the population variability, while conserving a relatively small computational cost. A method based on the composition of transformations is proposed to address this issue. The main novelties and key contributions of this paper are the definition of a transitivity error function and the presentation of an image clustering scheme that is based solely on the computed registration transformations. Leave-one-out experiments conducted on a database of \(N=50\) MR prostate scans demonstrate that a reduction of \((N-1)=49\)x in the number of pre-alignment registrations, and of 3.2x in term of total registration effort, is possible without significant impact on segmentation quality.

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Acknowledgments

This research was supported by the Cancer Council NSW (RG 11-05), the Prostate Cancer Foundation of Australia (YI2011), Movember and Cure Cancer Australia.

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Authors and Affiliations

  1. CSIRO The Australian e-Health Research Centre, Herston, QLD, Australia

    David Rivest-Hénault, Soumya Ghose, Jurgen Fripp & Jason A. Dowling

  2. Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands

    Josien P. W. Pluim

  3. Calvary Mater Newcastle Hospital, Newcastle, NSW, Australia

    Peter B. Greer

  4. University of Newcastle, Newcastle, NSW, Australia

    Peter B. Greer & Jason A. Dowling

Authors
  1. David Rivest-Hénault
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  2. Soumya Ghose
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  3. Josien P. W. Pluim
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  4. Peter B. Greer
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  5. Jurgen Fripp
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  6. Jason A. Dowling
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Corresponding author

Correspondence to David Rivest-Hénault .

Editor information

Editors and Affiliations

  1. Technische Universität München, München, Germany

    Bjoern Menze

  2. Medical University of Vienna, Vienna, Austria

    Georg Langs

  3. GE Global Research, Niskayuna, New York, USA

    Albert Montillo

  4. Siemens AG, Erlangen, Germany

    Michael Kelm

  5. University of Applied Sciences, Sierre, Switzerland

    Henning Müller

  6. University of North Carolina, Charlotte, USA

    Shaoting Zhang

  7. School of Information Technologies, Biomedical & Multimedia Info. Tech.(BMIT) Research Group, University of Sydney, Sydney, New South Wales, Australia

    Weidong (Tom) Cai

  8. Rutgers University, Piscataway, New Jersey, USA

    Dimitris Metaxas

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Rivest-Hénault, D., Ghose, S., Pluim, J.P.W., Greer, P.B., Fripp, J., Dowling, J.A. (2014). Fast Multiatlas Selection Using Composition of Transformations for Radiation Therapy Planning. In: Menze, B., et al. Medical Computer Vision: Algorithms for Big Data. MCV 2014. Lecture Notes in Computer Science(), vol 8848. Springer, Cham. https://doi.org/10.1007/978-3-319-13972-2_10

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  • DOI: https://doi.org/10.1007/978-3-319-13972-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13971-5

  • Online ISBN: 978-3-319-13972-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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