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. © 2011 Springer-Verlag.
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CITATION STYLE
Fouquier, G., Anquez, J., Bloch, I., Falip, C., & Adamsbaum, C. (2011). Subcutaneous adipose tissue segmentation in whole-body MRI of children. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7042 LNCS, pp. 97–104). https://doi.org/10.1007/978-3-642-25085-9_11
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