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 feed-forward neural networks are trained to perform the pixel-based segmentation. As features, we use parameters obtained from a pharmacokinetic model of the tissue perfusion (parametric images). Additional multiscale features that incorporate con-textual 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.