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AIME
2009
Springer

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

9 years 6 months ago
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...
Alessandro Daducci, Umberto Castellani, Marco Cris
Added 08 Nov 2010
Updated 08 Nov 2010
Type Conference
Year 2009
Where AIME
Authors Alessandro Daducci, Umberto Castellani, Marco Cristani, Paolo Farace, Pasquina Marzola, Andrea Sbarbati, Vittorio Murino
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