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In dynamic contrast-enhanced magnetic resonance imag- ing (DCE-MRI), segmentation of internal kidney structures like cortex, medulla and pelvo-caliceal cavities is necessary for functional assessment. Manual segmentation by a radiol- ogist is fairly delicate because images are blurred and highly noisy. Moreover the different compartments cannot be delin- eated on a single image because they are not visible during the same perfusion phase for physiological reasons. Nev- ertheless the differences between temporal evolution of con- trast in each anatomical region can be used to perform func- tional segmentation. We propose to test a semi-automated split and merge method based on time-intensity curves of re- nal 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 segmenta- tion by a radiologist is considered as a reference. Some dis- crepancy criteria are computed between this segmentation and the functional one. As a comparison, the same crite- ria are evaluated between the reference and another manual segmentation.

Functional Semi-Automated Segmentation of Renal DCE-MRI Sequences Using a Growing Neural Gas algorithm