title: Rapid assessment of liver volumetry by a novel automated segmentation algorithm.
abstract: OBJECTIVE
  This study aimed to evaluate a novel segmentation software for automated liver volumetry and segmentation regarding segmentation speed and interobserver variability.
  
  
  METHODS
  Computed tomographic scans of 20 patients without underlying liver disease and 10 patients with liver metastasis from colorectal cancer were analyzed by a novel segmentation software. Liver segmentation was performed after manual placement of specific landmarks into 9 segments according to the Couinaud model as well as into 4 segments, the latter being import for surgery planning. Time for segmentation was measured and the obtained segmental and total liver volumes between the different readers were compared calculating intraclass correlations (ICCs). Volumes of liver tumor burden were evaluated similarly.
  
  
  RESULTS
  Liver segmentation could be performed rapidly 3 minutes or less. Comparison of total liver volumes revealed a perfect ICC of greater than 0.997. Segmental liver volumes within the 9-part segmentation provided fair to moderate correlation for the left lobe and good to excellent correlations for the right lobe. When applying a 4-part segmentation relevant to clinical practice, strong to perfect agreement was observed. Similarly tumor volumes showed perfect ICC (>0.998).
  
  
  CONCLUSIONS
  Rapid determination of total and segmental liver volumes can be obtained using a novel segmentation software suitable for daily clinical practice.
authors.affiliation: Department of Diagnostic and Interventional Radiology, Klinikum Rechts der Isar, Technische Universität München, München, Germany.
authors.forename: Tina
authors.initials: T
authors.lastname: Zahel
authors.forename: Moritz
authors.initials: M
authors.lastname: Wildgruber
authors.forename: Roberto
authors.initials: R
authors.lastname: Ardon
authors.forename: Tibor
authors.initials: T
authors.lastname: Schuster
authors.forename: Ernst J
authors.initials: EJ
authors.lastname: Rummeny
authors.forename: Martin
authors.initials: M
authors.lastname: Dobritz
mesh_terms: D000465:Algorithms; D015179:Colorectal Neoplasms / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008113:Liver Neoplasms / Q000000981:diagnostic imaging* / Q000556:secondary*; D009929:Organ Size; D010363:Pattern Recognition, Automated / Q000379:methods; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*