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A semi-automated “blanket” method for renal segmentation from non-contrast T1-weighted MR images

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Abstract

Objective

To investigate the precision and accuracy of a new semi-automated method for kidney segmentation from single-breath-hold non-contrast MRI.

Materials and methods

The user draws approximate kidney contours on every tenth slice, focusing on separating adjacent organs from the kidney. The program then performs a sequence of fully automatic steps: contour filling, interpolation, non-uniformity correction, sampling of representative parenchyma signal, and 3D binary morphology. Three independent observers applied the method to images of 40 kidneys ranging in volume from 94.6 to 254.5 cm3. Manually constructed reference masks were used to assess accuracy.

Results

The volume errors for the three readers were: 4.4 % ± 3.0 %, 2.9 % ± 2.3 %, and 3.1 % ± 2.7 %. The relative discrepancy across readers was 2.5 % ± 2.1 %. The interactive processing time on average was 1.5 min per kidney.

Conclusions

Pending further validation, the semi-automated method could be applied for monitoring of renal status using non-contrast MRI.

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Acknowledgments

This work was supported by funding from the Diabetes Australia Research Trust and was performed under the rubric of the Center for Advanced Imaging Innovation and Research (www.cai2r.net), an NIBIB Biomedical Technology Resource Center (NIH P41 EB017183).

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Correspondence to Henry Rusinek.

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All volunteer and patient studies were performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.

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All volunteers gave their informed consent prior to their inclusion into the study.

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Rusinek, H., Lim, J.C., Wake, N. et al. A semi-automated “blanket” method for renal segmentation from non-contrast T1-weighted MR images. Magn Reson Mater Phy 29, 197–206 (2016). https://doi.org/10.1007/s10334-015-0504-5

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  • DOI: https://doi.org/10.1007/s10334-015-0504-5

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