Wavelet-based segmentation of renal compartments in DCE-MRI of human kidney: initial results in patients and healthy volunteers
Renal diseases can lead to kidney failure that requires life-long dialysis or renal transplantation. Early detection and treatment can prevent progression towards end stage renal disease. MRI has evolved into a standard examination for the assessment of the renal morphology and function. We propose...
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| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
March 2012
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| In: |
Computerized medical imaging and graphics
Year: 2012, Jahrgang: 36, Heft: 2, Pages: 108-118 |
| ISSN: | 1879-0771 |
| DOI: | 10.1016/j.compmedimag.2011.06.005 |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1016/j.compmedimag.2011.06.005 Verlag, Volltext: https://www.sciencedirect.com/science/article/pii/S0895611111000838 
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| Verfasserangaben: | Sheng Li, Frank G.Zöllner, Andreas D.Merrem, Yinghong Peng, Jarle Roervik, Arvid Lundervold, Lothar R.Schad |
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| 245 | 1 | 0 | |a Wavelet-based segmentation of renal compartments in DCE-MRI of human kidney |b initial results in patients and healthy volunteers |c Sheng Li, Frank G.Zöllner, Andreas D.Merrem, Yinghong Peng, Jarle Roervik, Arvid Lundervold, Lothar R.Schad |
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| 520 | |a Renal diseases can lead to kidney failure that requires life-long dialysis or renal transplantation. Early detection and treatment can prevent progression towards end stage renal disease. MRI has evolved into a standard examination for the assessment of the renal morphology and function. We propose a wavelet-based clustering to group the voxel time courses and thereby, to segment the renal compartments. This approach comprises (1) a nonparametric, discrete wavelet transform of the voxel time course, (2) thresholding of the wavelet coefficients using Stein's Unbiased Risk estimator, and (3) k-means clustering of the wavelet coefficients to segment the kidneys. Our method was applied to 3D dynamic contrast enhanced (DCE-) MRI data sets of human kidney in four healthy volunteers and three patients. On average, the renal cortex in the healthy volunteers could be segmented at 88%, the medulla at 91%, and the pelvis at 98% accuracy. In the patient data, with aberrant voxel time courses, the segmentation was also feasible with good results for the kidney compartments. In conclusion wavelet based clustering of DCE-MRI of kidney is feasible and a valuable tool towards automated perfusion and glomerular filtration rate quantification. | ||
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