Subcutaneous adipose tissue segmentation in whole-body MRI of children

Authors:
Geoffroy Fouquier;Jérémie Anquez;Isabelle Bloch;Céline Falip;Catherine Adamsbaum
Affiliations:
Telecom ParisTech, CNRS LTCI, and Whist Lab, Paris, France;Telecom ParisTech, CNRS LTCI, and Whist Lab, Paris, France;Telecom ParisTech, CNRS LTCI, and Whist Lab, Paris, France;Service de Radiologie Pédiatrique, Hôpital Saint Vincent de Paul, Paris, France;Service de Radiologie Pédiatrique, Hôpital Saint Vincent de Paul, Paris, France
Venue:
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Year:
2011

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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.