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Development and Evaluation of a Semi-automated Segmentation Tool and a Modified Ellipsoid Formula for Volumetric Analysis of the Kidney in Non-contrast T2-Weighted MR Images

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Abstract

Volumetric analysis of the kidney parenchyma provides additional information for the detection and monitoring of various renal diseases. Therefore the purposes of the study were to develop and evaluate a semi-automated segmentation tool and a modified ellipsoid formula for volumetric analysis of the kidney in non-contrast T2-weighted magnetic resonance (MR)-images. Three readers performed semi-automated segmentation of the total kidney volume (TKV) in axial, non-contrast-enhanced T2-weighted MR-images of 24 healthy volunteers (48 kidneys) twice. A semi-automated threshold-based segmentation tool was developed to segment the kidney parenchyma. Furthermore, the three readers measured renal dimensions (length, width, depth) and applied different formulas to calculate the TKV. Manual segmentation served as a reference volume. Volumes of the different methods were compared and time required was recorded. There was no significant difference between the semi-automatically and manually segmented TKV (p = 0.31). The difference in mean volumes was 0.3 ml (95% confidence interval (CI), −10.1 to 10.7 ml). Semi-automated segmentation was significantly faster than manual segmentation, with a mean difference = 188 s (220 vs. 408 s); p < 0.05. Volumes did not differ significantly comparing the results of different readers. Calculation of TKV with a modified ellipsoid formula (ellipsoid volume × 0.85) did not differ significantly from the reference volume; however, the mean error was three times higher (difference of mean volumes −0.1 ml; CI −31.1 to 30.9 ml; p = 0.95). Applying the modified ellipsoid formula was the fastest way to get an estimation of the renal volume (41 s). Semi-automated segmentation and volumetric analysis of the kidney in native T2-weighted MR data delivers accurate and reproducible results and was significantly faster than manual segmentation. Applying a modified ellipsoid formula quickly provides an accurate kidney volume.

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Acknowledgements

The authors thank the participants of this study.

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Authors and Affiliations

  1. Department of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Maximiliansplatz 1, 91054, Erlangen, Germany

    Hannes Seuss, Rolf Janka, Alexander Cavallaro, Ragnar Theis, Martin Sandmair, Tobias Bäuerle, Michael Uder & Matthias Hammon

  2. Chimaera GmbH, Am Weichselgarten 7, 91058, Erlangen, Germany

    Marcus Prümmer

  3. Department of Neurology, Klinikum Nuremberg, Breslauer Str. 201, 90471, Nuremberg, Germany

    Rebecca Hammon

  4. Department of Nephropathology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Krankenhausstr. 8-10, 91054, Erlangen, Germany

    Kerstin Amann

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  1. Hannes Seuss
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  2. Rolf Janka
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  4. Alexander Cavallaro
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  7. Martin Sandmair
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  9. Tobias Bäuerle
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  10. Michael Uder
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Correspondence to Matthias Hammon.

Ethics declarations

The institutional review board of the University Hospital Erlangen/Germany approved the study. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.

Funding

This research has been supported by the Smart Data Program in the KDI project of the Federal Ministry for Economic Affairs and Energy, Germany (01MT14001E).

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The authors declare that they have no conflict of interest.

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Seuss, H., Janka, R., Prümmer, M. et al. Development and Evaluation of a Semi-automated Segmentation Tool and a Modified Ellipsoid Formula for Volumetric Analysis of the Kidney in Non-contrast T2-Weighted MR Images. J Digit Imaging 30, 244–254 (2017). https://doi.org/10.1007/s10278-016-9936-3

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  • DOI: https://doi.org/10.1007/s10278-016-9936-3

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