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Comparison of Manual, Semi-Automatic, and Automatic CT-Based Methods for Liver Volume Segmentation.

Berna Dogan1, Sadik Bugrahan Simsek1, Sefa Sonmez1

  • 1Department of Anatomy, Faculty of Medicine, Tokat Gaziosmanpasa University, Tokat 60100, Türkiye.

Diagnostics (Basel, Switzerland)
|March 14, 2026
PubMed
Summary

Semi-automatic and automatic CT liver segmentation methods offer efficient alternatives to manual segmentation, maintaining clinical volumetric agreement. TotalSegmentator demonstrated the closest agreement, making it suitable for routine liver volumetry.

Keywords:
automatic segmentationcomputed tomographydeep learningliver volumesemi-automatic segmentation

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Area of Science:

  • Medical Imaging
  • Radiology
  • Computational Anatomy

Background:

  • Accurate liver volumetry is crucial for clinical decisions.
  • Manual segmentation is time-consuming and prone to variability.
  • Automated methods aim to improve efficiency and consistency.

Purpose of the Study:

  • To compare semi-automatic and automatic CT liver segmentation methods against manual segmentation.
  • To evaluate volumetric agreement and processing efficiency.
  • To identify optimal methods for routine clinical practice.

Main Methods:

  • Retrospective analysis of CT images from 86 individuals.
  • Liver volumes segmented manually and using RVX Semi-Automatic, RVX Deep Learning, and TotalSegmentator.
  • Statistical analysis included ANOVA, Bland-Altman analysis, Dice Similarity Coefficient (DICE), and Hausdorff Distance (HD).

Main Results:

  • RVX Deep Learning showed significantly higher volumes (p < 0.001); others had no significant difference.
  • TotalSegmentator and RVX Semi-Automatic demonstrated high volumetric agreement with manual segmentation.
  • RVX Deep Learning was fastest, manual segmentation was slowest; DICE scores were high for all automated methods.

Conclusions:

  • Semi-automatic and automatic liver segmentation significantly reduce processing time.
  • TotalSegmentator offers the closest agreement to manual segmentation for routine CT liver volumetry.
  • Deep learning methods are faster but may overestimate volumes, impacting precision-sensitive applications.