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Subcutaneous Adipose Tissue Segmentation in Whole-Body MRI of Children

  • Geoffroy Fouquier
  • Jérémie Anquez
  • Isabelle Bloch
  • Céline Falip
  • Catherine Adamsbaum
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)

Abstract

In this paper, we propose a new method to segment the subcutaneous adipose tissue (SAT) in whole-body (WB) magnetic resonance images of children. The method is based on an automated learning of radiometric characteristics, which is adaptive for each individual case, a decomposition of the body according to its main parts, and a minimal surface approach. The method aims at contributing to the creation of WB anatomical models of children, for applications such as numerical dosimetry simulations or medical applications such as obesity follow-up. Promising results are obtained on data from 20 children at various ages. Segmentations are validated with 4 manual segmentations.

Keywords

Body Part Manual Segmentation Chronic Recurrent Multifocal Osteomyelitis Body Silhouette IEEE PAMI 
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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Geoffroy Fouquier
    • 1
  • Jérémie Anquez
    • 1
  • Isabelle Bloch
    • 1
  • Céline Falip
    • 2
  • Catherine Adamsbaum
    • 2
    • 3
  1. 1.Telecom ParisTech, CNRS LTCI, and Whist LabParisFrance
  2. 2.Service de Radiologie PédiatriqueHôpital Saint Vincent de PaulParisFrance
  3. 3.Faculté de MédecineUniversité Paris DescartesParisFrance

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