Segmentation of bone tumor in MR perfusion images using neural networks and multiscale pharmacokinetic features

An approach for segmenting dynamic perfusion MR-images into viable tumor, nonviable tumor and healthy tissue is developed and experiments indicate that multiscale blurred versions of the parametric images together with a multiscales formulation of the local image entropy are the most discriminative features.

Abstract

The decrease in the volume of viable tumor is an indicator for the effect preoperative chemotherapy has on bone tumors. We develop an approach for segmenting dynamic perfusion MR-images into viable tumor, nonviable tumor and healthy tissue. Two cascaded feedforward neural networks are trained to perform the pixel-based segmentation. As features, we use the parameters obtained from a pharmacokinetic model of the tissue perfusion (parametric images). Additional multiscale features that incorporate contextual information are included. Experiments indicate that multiscale blurred versions of the parametric images together with a multiscale formulation of the local image entropy are the most discriminative features.