Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/16496
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dc.contributor.authorRusinek, Henry-
dc.contributor.authorLim, Jeremy C-
dc.contributor.authorWake, Nicole-
dc.contributor.authorSeah, Jas-mine-
dc.contributor.authorBotterill, Elissa-
dc.contributor.authorFarquharson, Shawna-
dc.contributor.authorMikheev, Artem-
dc.contributor.authorLim, Ruth P-
dc.date2015-10-29-
dc.date.accessioned2017-01-11T04:55:14Z-
dc.date.available2017-01-11T04:55:14Z-
dc.date.issued2016-04-
dc.identifier.citationMagnetic Resonance Materials in Physics, Biology and Medicine 2016; 29(2): 197-206en_US
dc.identifier.urihttp://ahro.austin.org.au/austinjspui/handle/1/16496-
dc.description.abstractOBJECTIVE: 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.en_US
dc.subjectKidneyen_US
dc.subjectMRIen_US
dc.subjectRenalen_US
dc.subjectSegmentationen_US
dc.subjectVolumeen_US
dc.titleA semi-automated "blanket" method for renal segmentation from non-contrast T1-weighted MR imagesen_US
dc.typeJournal Articleen_US
dc.identifier.journaltitleMagnetic Resonance Materials in Physics, Biology and Medicineen_US
dc.identifier.affiliationCenter for Advanced Imaging Innovation and Research (CAI2R) and Department of Radiology, New York University School of Medicine, New York, NY, USAen_US
dc.identifier.affiliationRadiology, Austin Health, Heidelberg, Victoria, Australiaen_US
dc.identifier.affiliationEndocrinology, Austin Health, Heidelberg, Victoria, Australiaen_US
dc.identifier.affiliationFlorey Neuroscience Institute, Melbourne, Victoria, Australiaen_US
dc.identifier.affiliationThe University of Melbourne, Melbourne, Victoria, Australiaen_US
dc.identifier.pubmedurihttps://pubmed.ncbi.nlm.nih.gov/26516082en_US
dc.identifier.doi10.1007/s10334-015-0504-5en_US
dc.type.contentTexten_US
dc.type.austinJournal Articleen_US
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