Bone tumor segmentation from MR perfusion images with neural networks using multi-scale pharmacokinetic features

Bone tumor segmentation and the distinction between viable and non-viable tumor tissue is required during the follow-up of chemotherapeutical treatment. Monitoring viable tumor area over time is important in the ongoing assessment of the effect of preoperative chemotherapy. In this paper, features derived from a pharmacokinetic model of tissue perfusion are investigated. A multi-scale analysis of the parametric perfusion images is applied to incorporate contextual information. A feed-forward neural network is proposed to classify pixels into viable, non-viable tumor, and healthy tissue. We elaborate on the design of a cascaded classifier and analyze the contribution of the different features to its performance. Multi-scale blurred versions of the parametric images together with a multi-scale formulation of the local image entropy turned out to be the most relevant features in distinguishing the tissues of interest. We experimented with an architecture consisting of cascaded neural networks to cope with uneven class distributions. The classification of each pixel was obtained by weighting the results of five bagged neural networks with either the mean or median rules. The experiments indicate that both the mean and median rules perform equally well.