DCE-MRI data analysis for cancer area classification.
OBJECTIVE
The paper aims at improving the support of medical researchers in the context of in-vivo cancer imaging. Morphological and functional parameters obtained by dynamic contrast-enhanced MRI (DCE-MRI) techniques are analyzed, which aim at investigating the development of tumor microvessels. The main contribution consists in proposing a machine learning methodology to segment automatically these MRI data, by isolating tumor areas with different meaning, in a histological sense.


METHODS
The proposed approach is based on a three-step procedure: i) robust feature extraction from raw time-intensity curves, ii) voxel segmentation, and iii) voxel classification based on a learning-by-example approach. In the first step, few robust features that compactly represent the response of the tissue to the DCE-MRI analysis are computed. The second step provides a segmentation based on the mean shift (MS) paradigm, which has recently shown to be robust and useful for different and heterogeneous clustering tasks. Finally, in the third step, a support vector machine (SVM) is trained to classify voxels according to the labels obtained by the clustering phase (i.e., each class corresponds to a cluster). Indeed, the SVM is able to classify new unseen subjects with the same kind of tumor.


RESULTS
Experiments on different subjects affected by the same kind of tumor evidence that the extracted regions by both the MS clustering and the SVM classifier exhibit a precise medical meaning, as carefully validated by the medical researchers. Moreover, our approach is more stable and robust than methods based on quantification of DCE-MRI data by means of pharmacokinetic models.


CONCLUSIONS
The proposed method allows to analyze the DCE-MRI data more precisely and faster than previous automated or manual approaches.
Castellani
Umberto
U
Department of Computer Science, University of Verona, Verona, Italy. umberto.castellani@univr.it
Cristiani
M
M
Daducci
A
A
Farace
P
P
Marzola
P
P
Murino
V
V
Sbarbati
A
A
D016000:Cluster Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009369:Neoplasms / Q000145:classification*