title: Automated Segmentation of Kidneys from MR Images in Patients with Autosomal Dominant Polycystic Kidney Disease.
abstract: OBJECTIVE
  Our study developed a fully automated method for segmentation and volumetric measurements of kidneys from magnetic resonance images in patients with autosomal dominant polycystic kidney disease and assessed the performance of the automated method with the reference manual segmentation method.
  
  
  METHODS
  Study patients were selected from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease. At the enrollment of the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease Study in 2000, patients with autosomal dominant polycystic kidney disease were between 15 and 46 years of age with relatively preserved GFRs. Our fully automated segmentation method was on the basis of a spatial prior probability map of the location of kidneys in abdominal magnetic resonance images and regional mapping with total variation regularization and propagated shape constraints that were formulated into a level set framework. T2-weighted magnetic resonance image sets of 120 kidneys were selected from 60 patients with autosomal dominant polycystic kidney disease and divided into the training and test datasets. The performance of the automated method in reference to the manual method was assessed by means of two metrics: Dice similarity coefficient and intraclass correlation coefficient of segmented kidney volume. The training and test sets were swapped for crossvalidation and reanalyzed.
  
  
  RESULTS
  Successful segmentation of kidneys was performed with the automated method in all test patients. The segmented kidney volumes ranged from 177.2 to 2634 ml (mean, 885.4±569.7 ml). The mean Dice similarity coefficient ±SD between the automated and manual methods was 0.88±0.08. The mean correlation coefficient between the two segmentation methods for the segmented volume measurements was 0.97 (P<0.001 for each crossvalidation set). The results from the crossvalidation sets were highly comparable.
  
  
  CONCLUSIONS
  We have developed a fully automated method for segmentation of kidneys from abdominal magnetic resonance images in patients with autosomal dominant polycystic kidney disease with varying kidney volumes. The performance of the automated method was in good agreement with that of manual method.
authors.affiliation: Departments of Radiology and.
authors.forename: Youngwoo
authors.initials: Y
authors.lastname: Kim
authors.affiliation: Departments of Radiology and.
authors.forename: Yinghui
authors.initials: Y
authors.lastname: Ge
authors.affiliation: Departments of Radiology and.
authors.forename: Cheng
authors.initials: C
authors.lastname: Tao
authors.affiliation: Departments of Radiology and.
authors.forename: Jianbing
authors.initials: J
authors.lastname: Zhu
authors.affiliation: Department of Internal Medicine, Emory University School of Medicine, Atlanta, Georgia;
authors.forename: Arlene B
authors.initials: AB
authors.lastname: Chapman
authors.affiliation: Department of Internal Medicine, Mayo College of Medicine, Rochester, Minnesota;
authors.forename: Vicente E
authors.initials: VE
authors.lastname: Torres
authors.affiliation: Department of Internal Medicine, Kansas University Medical Center, Kansas City, Kansas;
authors.forename: Alan S L
authors.initials: AS
authors.lastname: Yu
authors.affiliation: Division of Nephrology, University of Alabama, Birmingham, Alabama;
authors.forename: Michal
authors.initials: M
authors.lastname: Mrug
authors.affiliation: Legacy Good Samaritan Hospital, Portland, Oregon; and.
authors.forename: William M
authors.initials: WM
authors.lastname: Bennett
authors.affiliation: National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland.
authors.forename: Michael F
authors.initials: MF
authors.lastname: Flessner
authors.affiliation: Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania;
authors.forename: Doug P
authors.initials: DP
authors.lastname: Landsittel
authors.affiliation: Departments of Radiology and baek@upmc.edu.
authors.forename: Kyongtae T
authors.initials: KT
authors.lastname: Bae
mesh_terms: D000328:Adult; D005260:Female; D006801:Humans; D007089:Image Enhancement; D007668:Kidney / Q000000981:diagnostic imaging*; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D016891:Polycystic Kidney, Autosomal Dominant / Q000000981:diagnostic imaging*
keywords: ADPKD; Humans; Magnetic Resonance Imaging; Polycystic Kidney, Autosomal Dominant; genetic renal disease; glomerular filtration rate; kidney; kidney disease; kidney volume