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Background  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.    Objective  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.    Methods  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.    Results  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.    Conclusions  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.    Electronic supplementary material  The online version of this article (doi:10.1186/s13104-016-2292-z) contains supplementary material, which is available to authorized users.

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