Functional Segmentation of Renal DCE-MRI Sequences Using Vector Quantization Algorithms
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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.
KeywordsImage segmentation Vector quantization Biomedical image processing Biomedical magnetic resonance imaging Image sequence analysis Clustering methods
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- 3.Rohrschneider WK, Haufe S, Wiesel M, Toenshoff B, Wunsch R, Darge K, Clorius JH, Troeger J (2002) Functional and morphologic evaluation of congenital urinary tract dilatation by using combined static-dynamic MR urography: Findings in kidneys with a single collecting system. Radiology 224(3): 683–694CrossRefGoogle Scholar
- 5.Sun Y, Moura JMF, Chien H (2004) Subpixel registration in renal perfusion MR image sequence. In: Proceedings of the IEEE international symposium on biomedical imaging: macro to nano (ISBI 2004), vol 1. Arlington, VA, pp 700–703Google Scholar
- 7.Song T, Lee VS, Rusinek H, Wong S, Laine AF (2006) Four dimensional MR image analysis of dynamic renography. In: Proceedings of the 28th annual international conference of the IEEE engineering in medicine and biology society (EMBS 2006), pp 3134–3137Google Scholar
- 8.Chevaillier B, Collette JL, Mandry D, Claudon M, Pietquin O (2010) Objective assessment of renal DCE-MRI image segmentation. In: Proceedings of the 18th european signal processing conference (EUSIPCO 2010), Aalborg, Denmark, pp 1214–1218Google Scholar
- 10.Song T, Lee VS, Rusinek H, Sajous JB, Laine AF (2005) Registration and segmentation of dynamic three-dimensional MR renography based on Fourier representations and k-means clustering. In: Proceedings of the 13th scientific meeting of the international society for magnetic resonance in medicine (ISMRM 2005), Miami, Florida, p 2266Google Scholar
- 11.Chevaillier B, Ponvianne Y, Collette J, Mandry D, Claudon M, Pietquin O (2008) Functional semi-automated segmentation of renal DCE-MRI sequences. In: Proceedings of the 33rd IEEE international conference on acoustics, speech and signal processing (ICASSP 2008), Las Vegas, NV, pp 525–528Google Scholar
- 12.Chevaillier B, Ponvianne Y, Collette J, Mandry D, Claudon M, Pietquin O (2008) Functional semi-automated segmentation of renal DCE-MRI sequences using a growing neural gas algorithm. In: Proceedings of the 16th European signal processing conference (EUSIPCO 2008), Lausanne, Switzerland (Electronic proceedings), p 4Google Scholar
- 15.Fritzke B (1995) A growing neural gas network learns topologies. In: Tesauro G, Touretzky DS, Leen TK (eds) Advances in neural information processing systems 7. MIT Press, Cambridge, pp 625–632Google Scholar