NDLI: Functional Segmentation of Renal DCE-MRI Sequences Using Vector Quantization Algorithms

Content Provider	Springer Nature Link
Author	Chevaillier, Béatrice Mandry, Damien Collette, Jean Luc Claudon, Michel Galloy, Marie Agnès Pietquin, Olivier
Copyright Year	2011
Abstract	In dynamic contrast-enhanced magnetic resonance imaging, segmentation of internal kidney structures like cortex, medulla and cavities is essential for functional assessment. To avoid fastidious and time-consuming manual segmentation, semi-automatic methods have been recently developed. Some of them use the differences between temporal contrast evolution in each anatomical region to perform functional segmentation. We test two methods where pixels are classified according to their time–intensity evolution. They both require a vector quantization stage with some unsupervised learning algorithm (K-means or Growing Neural Gas with targeting). Three or more classes are thus obtained. In the first case the method is completely automatic. In the second case, a restricted intervention by an observer is required for merging. As no ground truth is available for result evaluation, a manual anatomical segmentation is considered as a reference. Some discrepancy criteria like overlap, extra pixels and similarity index are computed between this segmentation and a functional one. The same criteria are also evaluated between the reference and another manual segmentation. Results are comparable for the two types of comparisons, proving that anatomical segmentation can be performed using functional information.
Starting Page	71
Ending Page	85
Page Count	15
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	34
Issue Number	1
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2011-05-24
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	Image segmentation Vector quantization Biomedical image processing Biomedical magnetic resonance imaging Image sequence analysis Clustering methods Computational Intelligence Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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