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Magnetic Resonance Materials in Physics, Biology and Medicine

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29.10.2015 | Research Article

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

verfasst von: Henry Rusinek, Jeremy C. Lim, Nicole Wake, Jas-mine Seah, Elissa Botterill, Shawna Farquharson, Artem Mikheev, Ruth P. Lim

Erschienen in: Magnetic Resonance Materials in Physics, Biology and Medicine | Ausgabe 2/2016

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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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Titel: A semi-automated “blanket” method for renal segmentation from non-contrast T1-weighted MR images
verfasst von: Henry Rusinek
Jeremy C. Lim
Nicole Wake
Jas-mine Seah
Elissa Botterill
Shawna Farquharson
Artem Mikheev
Ruth P. Lim
Publikationsdatum: 29.10.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Magnetic Resonance Materials in Physics, Biology and Medicine / Ausgabe 2/2016
Print ISSN: 0968-5243
Elektronische ISSN: 1352-8661
DOI: https://doi.org/10.1007/s10334-015-0504-5

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

Abstract

Objective

Materials and methods

Results

Conclusions

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Abstract

Objective

Materials and methods

Results

Conclusions

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