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

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

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Additional file 1. unimodcode.zip: a zipped folder containing the following files: unimodscript.py: the central script for the unimodal thresholding approach, l03threshSTD.py: the monomodal thresholding algorithm function, l02shrink.py: a function to remove most of the whitespace from a masked volume, detect_peaks.py: the peak detection function by Marcos Duarte, also available at https://github.com/demotu/BMC/blob/master/functions/detect_peaks.py , example.nii.gz: an anonymized abdominal MRI in NIfTI format, example_left_kidney_preseg.nii.gz: a manual pre-segmentation of the left kidney of the example MRI, done with the Multi-image Analysis GUI (MANGO), available at http://ric.uthscsa.edu/mango/ , example_right_kidney_preseg.nii.gz: a manual pre-segmentation of the right kidney of the example MRI, done with the Multi-image Analysis GUI (MANGO), available at http://ric.uthscsa.edu/mango/ , LICENSE.txt: the MIT license text, readme.txt: description of the included files, list of dependencies, instructions for installation and use