Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/16496
Title: A semi-automated "blanket" method for renal segmentation from non-contrast T1-weighted MR images
Austin Authors: Rusinek, Henry;Lim, Jeremy C;Wake, Nicole;Seah, Jas-mine;Botterill, Elissa;Farquharson, Shawna ;Mikheev, Artem;Lim, Ruth P 
Affiliation: Center for Advanced Imaging Innovation and Research (CAI2R) and Department of Radiology, New York University School of Medicine, New York, NY, USA
Radiology, Austin Health, Heidelberg, Victoria, Australia
Endocrinology, Austin Health, Heidelberg, Victoria, Australia
Florey Neuroscience Institute, Melbourne, Victoria, Australia
The University of Melbourne, Melbourne, Victoria, Australia
Issue Date: Apr-2016
metadata.dc.date: 2015-10-29
Publication information: Magnetic Resonance Materials in Physics, Biology and Medicine 2016; 29(2): 197-206
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 cm(3). 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.
URI: http://ahro.austin.org.au/austinjspui/handle/1/16496
DOI: 10.1007/s10334-015-0504-5
PubMed URL: https://pubmed.ncbi.nlm.nih.gov/26516082
Type: Journal Article
Subjects: Kidney
MRI
Renal
Segmentation
Volume
Appears in Collections:Journal articles

Show full item record

Google ScholarTM

Check


Items in AHRO are protected by copyright, with all rights reserved, unless otherwise indicated.