Functional semi-automated segmentation of renal DCE-MRI sequences using a Growing Neural Gas algorithm

In dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), segmentation of internal kidney structures like cortex, medulla and pelvo-caliceal cavities is necessary for functional assessment. Manual segmentation by a radiologist is fairly delicate because images are blurred and highly noisy. Moreover the different compartments cannot be delineated on a single image because they are not visible during the same perfusion phase for physiological reasons. Nevertheless the differences between temporal evolution of contrast in each anatomical region can be used to perform functional segmentation. We propose to test a semi-automated split and merge method based on time-intensity curves of renal pixels. Its first step requires a variant of the classical Growing Neural Gas algorithm. In the absence of ground truth for results assessment, a manual anatomical segmentation by a radiologist is considered as a reference. Some discrepancy criteria are computed between this segmentation and the functional one. As a comparison, the same criteria are evaluated between the reference and another manual segmentation.