  Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is a valuable
tool to localize, characterize, and evaluate anomalous prostate tissue.
Ultrafast gradient-echo acquisitions of MRI volumes are generated at regular
time intervals while the patient receives a paramagnetic contrast agent. The
result is a time series where each voxel exhibits a particular behavior of
contrast uptake (wash-in) and posterior wash-out. In this work, a segmentation
method is proposed that groups together neighboring voxels with similar
contrast wash-out responses, using a variant of the region growing GrowCut
cellular automaton algorithm, that evolves iteratively according to the
relationship of a cell to its neighboring cells. Initially, seed cells are
determined through morphological filters that identify homogeneous regions in
the volume that are representatives of the clinically relevant types of tissues
in the prostate. Each cell is characterized by three parameters: a label that
identifies the type of tissue in the associated voxel, a vector with the values
of the DCE-MRI time series, and a strength coefficient with values between 0
and 1 that represents the probability with which the cell belongs to its
assigned label. Every non-seed cell can modify its state; this occurs when each
cell is attacked by its neighbors with a strength of attack that is inversely
proportional to the similarity of the values of the time series between the
cells. If the strength of the attacked cell is less than the strength of the
attack of one of its neighbors, its state changes to the one of the attacking
cell. The attacked cell also updates its strength making it equal to the
strength of the attack with which it was conquered. To perform a clinical
validation of the resulting segmentations, we used various cases from the
database of The Cancer Imaging Archive (TCIA), National Cancer Institute (NCI).
