title: Computer-aided liver volumetry: performance of a fully-automated, prototype post-processing solution for whole-organ and lobar segmentation based on MDCT imaging.
abstract: OBJECTIVE
  To evaluate the performance of a prototype, fully-automated post-processing solution for whole-liver and lobar segmentation based on MDCT datasets.
  
  
  METHODS
  A polymer liver phantom was used to assess accuracy of post-processing applications comparing phantom volumes determined via Archimedes' principle with MDCT segmented datasets. For the IRB-approved, HIPAA-compliant study, 25 patients were enrolled. Volumetry performance compared the manual approach with the automated prototype, assessing intraobserver variability, and interclass correlation for whole-organ and lobar segmentation using ANOVA comparison. Fidelity of segmentation was evaluated qualitatively.
  
  
  RESULTS
  Phantom volume was 1581.0 ± 44.7 mL, manually segmented datasets estimated 1628.0 ± 47.8 mL, representing a mean overestimation of 3.0%, automatically segmented datasets estimated 1601.9 ± 0 mL, representing a mean overestimation of 1.3%. Whole-liver and segmental volumetry demonstrated no significant intraobserver variability for neither manual nor automated measurements. For whole-liver volumetry, automated measurement repetitions resulted in identical values; reproducible whole-organ volumetry was also achieved with manual segmentation, p(ANOVA) 0.98. For lobar volumetry, automated segmentation improved reproducibility over manual approach, without significant measurement differences for either methodology, p(ANOVA) 0.95-0.99. Whole-organ and lobar segmentation results from manual and automated segmentation showed no significant differences, p(ANOVA) 0.96-1.00. Assessment of segmentation fidelity found that segments I-IV/VI showed greater segmentation inaccuracies compared to the remaining right hepatic lobe segments.
  
  
  CONCLUSIONS
  Automated whole-liver segmentation showed non-inferiority of fully-automated whole-liver segmentation compared to manual approaches with improved reproducibility and post-processing duration; automated dual-seed lobar segmentation showed slight tendencies for underestimating the right hepatic lobe volume and greater variability in edge detection for the left hepatic lobe compared to manual segmentation.
authors.affiliation: Department of Radiology, Duke University Medical Center, DUMC 3808, Durham, NC, 27710, USA.
authors.forename: Ghaneh
authors.initials: G
authors.lastname: Fananapazir
authors.forename: Mustafa R
authors.initials: MR
authors.lastname: Bashir
authors.forename: Daniele
authors.initials: D
authors.lastname: Marin
authors.forename: Daniel T
authors.initials: DT
authors.lastname: Boll
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D008099:Liver / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D061330:Multidetector Computed Tomography; D009929:Organ Size; D019047:Phantoms, Imaging; D011857:Radiographic Image Interpretation, Computer-Assisted