Abstract
Purpose
To evaluate the performance of a prototype, fully-automated post-processing solution for whole-liver and lobar segmentation based on MDCT datasets.
Materials and 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.
Conclusion
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.
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Fananapazir, G., Bashir, M.R., Marin, D. et al. Computer-aided liver volumetry: performance of a fully-automated, prototype post-processing solution for whole-organ and lobar segmentation based on MDCT imaging. Abdom Imaging 40, 1203–1212 (2015). https://doi.org/10.1007/s00261-014-0276-9
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DOI: https://doi.org/10.1007/s00261-014-0276-9