Introduction of a combination vector to optimise the interpolation of numerical phantoms.
Fiche publication
Date publication
février 2013
Auteurs
Membres identifiés du Cancéropôle Est :
Dr HENRIET Julien
Tous les auteurs :
Henriet J, Chatonnay P
Lien Pubmed
Résumé
Phantoms are 3-dimensional (3D) numerical representations of the contours of organs in the human body. The quality of the dosimetric reports established when accidental overexposures to radiation occur is highly dependent on the phantom's reliability with respect to the subject. EquiVox is a Case-Based Reasoning platform which proposes an interpolation of the 3D Lung Contours (3DLC) of subjects during its adaptation phase. This interpolation is conducted by an Artificial Neural Network (ANN) trained to learn how to interpolate the 3DLC of a learning set (LS). ANN is a well-suited tool when known results are numerous. Since the cardinality of our learning set is restrained, the imperfections of each 3DLC have a great impact on interpolations. Thus, we explored the possibility of ignoring some of the 3DLC of LS via implementation of a new learning algorithm which associated Combination Vectors (CV) to LS. The results proved that this method could optimise interpolation accuracy. Furthermore, this study highlights the fact that some of the 3DLC were harmful for some interpolations whereas they increased the accuracy of others. (C) 2012 Elsevier Ltd. All rights reserved.
Référence
Expert Syst Appl. 2013 Feb 1;40(2):492-9.