Summary
Objectives: 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.
Keywords
DCE-MRI - cluster analysis - classification - SVM
