title: Hepatic vessel segmentation for 3D planning of liver surgery experimental evaluation of a new fully automatic algorithm.
abstract: OBJECTIVE
  The aim of this study was to identify the optimal parameter configuration of a new algorithm for fully automatic segmentation of hepatic vessels, evaluating its accuracy in view of its use in a computer system for three-dimensional (3D) planning of liver surgery.
  
  
  METHODS
  A phantom reproduction of a human liver with vessels up to the fourth subsegment order, corresponding to a minimum diameter of 0.2 mm, was realized through stereolithography, exploiting a 3D model derived from a real human computed tomographic data set. Algorithm parameter configuration was experimentally optimized, and the maximum achievable segmentation accuracy was quantified for both single two-dimensional slices and 3D reconstruction of the vessel network, through an analytic comparison of the automatic segmentation performed on contrast-enhanced computed tomographic phantom images with actual model features.
  
  
  RESULTS
  The optimal algorithm configuration resulted in a vessel detection sensitivity of 100% for vessels > 1 mm in diameter, 50% in the range 0.5 to 1 mm, and 14% in the range 0.2 to 0.5 mm. An average area overlap of 94.9% was obtained between automatically and manually segmented vessel sections, with an average difference of 0.06 mm(2). The average values of corresponding false-positive and false-negative ratios were 7.7% and 2.3%, respectively.
  
  
  CONCLUSIONS
  A robust and accurate algorithm for automatic extraction of the hepatic vessel tree from contrast-enhanced computed tomographic volume images was proposed and experimentally assessed on a liver model, showing unprecedented sensitivity in vessel delineation. This automatic segmentation algorithm is promising for supporting liver surgery planning and for guiding intraoperative resections.
authors.affiliation: Biomedical Engineering, Science and Technology Division, Institute of Clinical Physiology, National Research Council, Campus Ecotekne, Lecce, Italy. conversano@ifc.cnr.it
authors.forename: Francesco
authors.initials: F
authors.lastname: Conversano
authors.forename: Roberto
authors.initials: R
authors.lastname: Franchini
authors.forename: Christian
authors.initials: C
authors.lastname: Demitri
authors.forename: Laurent
authors.initials: L
authors.lastname: Massoptier
authors.forename: Francesco
authors.initials: F
authors.lastname: Montagna
authors.forename: Alfonso
authors.initials: A
authors.lastname: Maffezzoli
authors.forename: Antonio
authors.initials: A
authors.lastname: Malvasi
authors.forename: Sergio
authors.initials: S
authors.lastname: Casciaro
mesh_terms: D000465:Algorithms; D006498:Hepatectomy / Q000379:methods*; D006499:Hepatic Artery / Q000000981:diagnostic imaging* / Q000601:surgery*; D006503:Hepatic Veins / Q000000981:diagnostic imaging* / Q000601:surgery*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D019047:Phantoms, Imaging; D011300:Preoperative Care / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D025321:Surgery, Computer-Assisted / Q000379:methods; D014057:Tomography, X-Ray Computed / Q000379:methods