title: A semi-automated "blanket" method for renal segmentation from non-contrast T1-weighted MR images.
abstract: OBJECTIVE
  To investigate the precision and accuracy of a new semi-automated method for kidney segmentation from single-breath-hold non-contrast MRI.
  
  
  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.
authors.affiliation: Center for Advanced Imaging Innovation and Research (CAI2R) and Department of Radiology, New York University School of Medicine, 660 1st Avenue, Rm413, New York, NY, 10016, USA. hr18@nyu.edu.
authors.forename: Henry
authors.identifier: http://orcid.org/0000-0001-6035-6314
authors.initials: H
authors.lastname: Rusinek
authors.affiliation: Radiology, Austin Health, Melbourne, VIC, Australia.
authors.forename: Jeremy C
authors.initials: JC
authors.lastname: Lim
authors.affiliation: Center for Advanced Imaging Innovation and Research (CAI2R) and Department of Radiology, New York University School of Medicine, 660 1st Avenue, Rm413, New York, NY, 10016, USA.
authors.forename: Nicole
authors.initials: N
authors.lastname: Wake
authors.affiliation: Endocrinology, Austin Health, Melbourne, VIC, Australia.
authors.forename: Jas-mine
authors.initials: JM
authors.lastname: Seah
authors.affiliation: Radiology, Austin Health, Melbourne, VIC, Australia.
authors.forename: Elissa
authors.initials: E
authors.lastname: Botterill
authors.affiliation: Florey Neuroscience Institute, Melbourne, VIC, Australia.
authors.forename: Shawna
authors.initials: S
authors.lastname: Farquharson
authors.affiliation: Center for Advanced Imaging Innovation and Research (CAI2R) and Department of Radiology, New York University School of Medicine, 660 1st Avenue, Rm413, New York, NY, 10016, USA.
authors.forename: Artem
authors.initials: A
authors.lastname: Mikheev
authors.affiliation: Radiology, Austin Health, Melbourne, VIC, Australia.|The University of Melbourne, Melbourne, VIC, Australia.
authors.forename: Ruth P
authors.initials: RP
authors.lastname: Lim
mesh_terms: D000328:Adult; D000465:Algorithms; D003287:Contrast Media; D003924:Diabetes Mellitus, Type 2 / Q000000981:diagnostic imaging* / Q000473:pathology; D003928:Diabetic Nephropathies / Q000000981:diagnostic imaging* / Q000473:pathology; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007668:Kidney / Q000000981:diagnostic imaging* / Q000473:pathology; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D009929:Organ Size; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface
chemical_list: D003287:Contrast Media
keywords: Kidney; MRI; Renal; Segmentation; Volume