Learning Approach to Analyze Tumour Heterogeneity in DCE-MRI Data During Anti-cancer Treatment

Abstract. The paper proposes a learning approach to support medical researchers in the context of in-vivo cancer imaging, and specifically in the analysis of Dynamic Contrast-Enhanced MRI (DCE-MRI) data. DCE-MRI techniques are applied to monitor the development of the tumour micro-vessels. Tumour heterogeneity is characterized by identifying regions with different vascular perfusion. The overall aim is to measure volume differences of such regions for two experimental groups: the treated group, to which an anticancer therapy is administered, and a control group. In this way a non-invasive method for the analysis of the treatment efficacy is obtained. The proposed approach is based on a three-steps procedure: (i) robust features extraction from raw timeintensity curves, (ii) sample-regions identification manually traced by medical researchers on a small portion of input data, and (iii) overall segmentation by training a Support Vector Machine (SVM) to classify the MRI voxels according t...

• Type: Conference
• Year: 2009
• Where: AIME
• Authors: Alessandro Daducci, Umberto Castellani, Marco Cristani, Paolo Farace, Pasquina Marzola, Andrea Sbarbati, Vittorio Murino
• Keywords: AIME 2009, Artificial Intelligence, Medical Researchers, Tumour Heterogeneity, Tumour Micro-vessels