Semiautomatic segmentation of the kidney in magnetic resonance images using unimodal thresholding

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Details zur Publikation

Autorinnen und Autoren: Sandmair M, Hammon M, Seuss H, Theis R, Uder M, Janka RM
Zeitschrift: BMC Research Notes
Jahr der Veröffentlichung: 2016
Band: 9
Heftnummer: 1
Seitenbereich: 489
ISSN: 1756-0500


Abstract


Total kidney volume (TKV) is an important marker for the presence or progression of chronic kidney disease, however, routine ultrasonography underestimates renal volume to a high and varying degree.The aim of this work was to adapt and evaluate a semi-automatic unimodal thresholding method for volumetric analysis of the kidney in native T2-weighted magnetic resonance (MR) images.In a group of healthy volunteers (n = 24; 48 kidneys), we defined a region of interest (ROI) by manually tracing the outline of the kidney in every MR image. An automatic unimodal thresholding algorithm with visual feedback was applied to the probability distribution function of voxel intensities in the ROI to remove intrarenal non-parenchyma volume. For comparison, reference volumes were created by manual segmentation. Intra- and inter-observer reliability was evaluated.There was a small, significant mean difference of 1.5 ml between semi-automatically and manually segmented TKV (p = 0.009, 95% CI [0.4, 2.7]). While intra-observer reliability was good (mean difference 2.9 ml, p < 0.01, 95% CI [1.5, 4.2]) there was a small but significant mean difference of 4.8 ml (p < 0.01, 95% CI [3.6, 5.9]) between the TKV results of different observers. Reference volume correlations were excellent (r = 0.97-0.98). Semi-automated segmentation was significantly faster than manual segmentation; mean difference = 234 s [91-483 s]; p < 0.05. Automatic unimodal thresholding removed a considerable mean volume of 18.7 ml (13.1%) from the coarse manual pre-segmentations.Unimodal thresholding of native MR images is a robust and sufficiently reliable method for kidney segmentation and volumetric analysis. The manual pre-segmentation can be done by non-experts with little introduction.



FAU-Autorinnen und Autoren / FAU-Herausgeberinnen und Herausgeber

Hammon, Matthias PD Dr.
Radiologisches Institut
Janka, Rolf Matthias Prof. Dr.
Radiologisches Institut
Sandmair, Martin
Lehrstuhl für Diagnostische Radiologie
Uder, Michael Prof. Dr.
Lehrstuhl für Diagnostische Radiologie


Zitierweisen

APA:
Sandmair, M., Hammon, M., Seuss, H., Theis, R., Uder, M., & Janka, R.M. (2016). Semiautomatic segmentation of the kidney in magnetic resonance images using unimodal thresholding. BMC Research Notes, 9(1), 489. https://dx.doi.org/10.1186/s13104-016-2292-z

MLA:
Sandmair, Martin, et al. "Semiautomatic segmentation of the kidney in magnetic resonance images using unimodal thresholding." BMC Research Notes 9.1 (2016): 489.

BibTeX: 

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