title: Liver segmentation in living liver transplant donors: comparison of semiautomatic and manual methods.
abstract: OBJECTIVE
  To compare the accuracy and repeatability of a semiautomatic segmentation algorithm with those of manual segmentation for determining liver volume in living liver transplant donors at magnetic resonance (MR) imaging.
  
  
  METHODS
  The institutional review board approved this retrospective study and waived the requirement for informed consent. The semiautomatic segmentation algorithm is based on geometric deformable models and the level-set technique. It entails (a) placing initialization circle(s) on each image section, (b) running the algorithm, (c) inspecting and possibly manually modifying the contours obtained with the segmentation algorithm, and (d) placing lines to separate the liver segments. For 18 living donors (eight men and 10 women; mean age, 34 years; age range, 25-46 years), two observers each performed two semiautomatic and two manual segmentations on contrast material-enhanced T1-weighted MR images. Each measurement was timed. Actual graft weight was measured during surgery. The time needed for manual and that needed for semiautomatic segmentation were compared. Accuracy and repeatability were evaluated with the Bland-Altman method.
  
  
  RESULTS
  Mean interaction time was reduced from 25 minutes with manual segmentation to 5 minutes with semiautomatic segmentation. The mean total time for the semiautomatic process was 7 minutes 20 seconds. Differences between the actual volume and the estimated volume ranged from -223 to +123 mL for manual segmentation and from -214 to +86 mL for semiautomatic segmentation. The 95% limits of agreement for the ratio of actual graft volume to estimated graft volume were 0.686 and 1.601 for semiautomatic segmentation and 0.651 and 1.957 for manual segmentation. Semiautomatic segmentation improved estimation in 15 of 18 cases. Inter- and intraobserver repeatability was higher with semiautomatic segmentation.
  
  
  CONCLUSIONS
  Use of the semiautomatic segmentation algorithm substantially reduces the time needed for volumetric measurement of liver segments while improving both accuracy and repeatability.
authors.affiliation: Diagnostic Radiology Unit and Center for Anatomical, Functional and Molecular Imaging Research, Université Catholique de Louvain, Saint-Luc University Hospital, Avenue Hippocrate 10, B-1200 Brussels, Belgium. hermoye@rdgn.ucl.ac.be
authors.forename: Laurent
authors.initials: L
authors.lastname: Hermoye
authors.forename: Ismael
authors.initials: I
authors.lastname: Laamari-Azjal
authors.forename: Zhujiang
authors.initials: Z
authors.lastname: Cao
authors.forename: Laurence
authors.initials: L
authors.lastname: Annet
authors.forename: Jan
authors.initials: J
authors.lastname: Lerut
authors.forename: Benoit M
authors.initials: BM
authors.lastname: Dawant
authors.forename: Bernard E
authors.initials: BE
authors.lastname: Van Beers
mesh_terms: D000328:Adult; D000465:Algorithms; D000704:Analysis of Variance; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008099:Liver / Q000033:anatomy & histology*; D016031:Liver Transplantation; D019520:Living Donors; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008432:Mathematical Computing; D008875:Middle Aged; D015203:Reproducibility of Results; D012189:Retrospective Studies