@InProceedings{Supelec385,
author = {Beatrice Chevaillier and Yannick Ponvianne and Jean-Luc Collette and Damien Mandry and Michel Claudon and Olivier Pietquin},
title = {Functional Semi-Automated Segmentation of Renal DCE-MRI Sequences},
year = {2008},
booktitle = {Proceedings of the 33rd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008)},
pages = {525-528},
month = {April},
address = {Las Vegas (NV, USA)},
url = {http://hal-supelec.archives-ouvertes.fr/hal-00276131/fr/},
abstract = {In dynamic contrast-enhanced magnetic resonance imaging (DCE- MRI), segmentation of internal kidney structures is essential for functional evaluation. Manual morphological segmentation of cortex, medulla and cavities remains difficult and time- consuming especially because the different renal compartments are hard to distinguish on a single image. We propose to test a semi-automated method to segment internal kidney structures from a DCE-MRI registered sequence. As the temporal intensity evolution is different in each of the three kidney compartments, pixels are sorted according to their time- intensity curves using a k-means partitioning algorithm. No ground truth is available to evaluate resulting segmentations so a manual segmentation by a radiologist is chosen as a reference. We first evaluate some similarity criteria between the functional segmentations and this reference. The same measures are then computed between another manual segmentation and the reference. Results are similar for the two types of comparisons. }
}