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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 thres...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5114781/ https://www.ncbi.nlm.nih.gov/pubmed/27855691 http://dx.doi.org/10.1186/s13104-016-2292-z |
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author | Sandmair, Martin Hammon, Matthias Seuss, Hannes Theis, Ragnar Uder, Michael Janka, Rolf |
author_facet | Sandmair, Martin Hammon, Matthias Seuss, Hannes Theis, Ragnar Uder, Michael Janka, Rolf |
author_sort | Sandmair, Martin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5114781 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-51147812016-11-25 Semiautomatic segmentation of the kidney in magnetic resonance images using unimodal thresholding Sandmair, Martin Hammon, Matthias Seuss, Hannes Theis, Ragnar Uder, Michael Janka, Rolf BMC Res Notes Research Article 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. BioMed Central 2016-11-17 /pmc/articles/PMC5114781/ /pubmed/27855691 http://dx.doi.org/10.1186/s13104-016-2292-z Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Sandmair, Martin Hammon, Matthias Seuss, Hannes Theis, Ragnar Uder, Michael Janka, Rolf Semiautomatic segmentation of the kidney in magnetic resonance images using unimodal thresholding |
title | Semiautomatic segmentation of the kidney in magnetic resonance images using unimodal thresholding |
title_full | Semiautomatic segmentation of the kidney in magnetic resonance images using unimodal thresholding |
title_fullStr | Semiautomatic segmentation of the kidney in magnetic resonance images using unimodal thresholding |
title_full_unstemmed | Semiautomatic segmentation of the kidney in magnetic resonance images using unimodal thresholding |
title_short | Semiautomatic segmentation of the kidney in magnetic resonance images using unimodal thresholding |
title_sort | semiautomatic segmentation of the kidney in magnetic resonance images using unimodal thresholding |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5114781/ https://www.ncbi.nlm.nih.gov/pubmed/27855691 http://dx.doi.org/10.1186/s13104-016-2292-z |
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