Abstract
Children obesity is a growing concern in the healthcare system, because a life of dependence and cronic health problems results in the adult phase of life. Non-alcoholic liver fat and visceral adiposity are two biomarkers of the health status of the child. Some studies try to measure the impact of exercise and improved habits in the reduction of these biomarkers. The studies use a fat enhancing magnetic resonance imaging sequence, but visceral fat is difficult to segment manually. In this paper we describe the automated process that we have devised, providing some visual results. The process has three main phases: (a) image inhomogeneity correction, (b) removal of spurious features such as arms and navel, (c) identification of the peripheral and visceral volumes, (d) localization of the intervertebral disks, which need to be removed. As there is no manual tracing to use as gold standard, we can not provide quantitative performance measures.
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This research has been partially funded the Basque Government grant IT874-13 for the GIC research group.
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Graña, M., Echaniz, O., Rodriguez-Vigil, B., Labayen, I. (2018). Automated Segmentation of Visceral Adiposity in MRI in Obese Children. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-59162-9_40
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DOI: https://doi.org/10.1007/978-3-319-59162-9_40
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