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Related Experiment Video

Updated: Jul 15, 2025

Laparoscopic Anatomical Liver Segment VII Resection with Liver Parenchymal Transection Following a Priority Approach
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Laparoscopic Anatomical Liver Segment VII Resection with Liver Parenchymal Transection Following a Priority Approach

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Two-step artificial intelligence algorithm for liver segmentation automates anatomic virtual hepatectomy.

Yusuke Kazami1, Junichi Kaneko1, Deepak Keshwani2

  • 1Hepato-Biliary-Pancreatic Surgery Division, Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

Journal of Hepato-Biliary-Pancreatic Sciences
|September 25, 2023
PubMed
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This summary is machine-generated.

This study introduces an automated artificial intelligence (AI) algorithm for precise liver segmentation, significantly improving virtual hepatectomy planning. The AI accurately divides liver sections, enhancing surgical preparation for liver transplantation.

Area of Science:

  • Medical Imaging
  • Surgical Planning
  • Artificial Intelligence

Background:

  • Precise liver segmentation for virtual hepatectomy is crucial but not automated with conventional 3D simulation.
  • Artificial intelligence (AI) has not yet been applied to automate liver segmentation for surgical planning.

Purpose of the Study:

  • To develop and validate a novel two-step AI algorithm for automated liver segmentation.
  • To assess the accuracy of AI-driven liver segmentation compared to manual segmentation.

Main Methods:

  • A two-step AI algorithm was developed using CT data from 174 living-donor candidates.
  • The algorithm was validated on CT data from an additional 51 donors.
  • AI-based segmentation (Pure-AI) was compared against ground truth (GT) manual segmentation.
Keywords:
anatomic virtual hepatectomyartificial intelligenceautomatic liver segmentationautomatic liver vessel extractiondeep learning

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Main Results:

  • The AI algorithm achieved high Dice coefficients for hemilivers (0.95-0.92) and sectors (0.86-0.92).
  • Accurate segmentation was observed for Couinaud's segments (0.71-0.89).
  • The AI perfectly matched hemiliver and portal vein labeling, with over 90% accuracy for sectors and segments compared to GT.

Conclusions:

  • The developed AI algorithm successfully automates liver segmentation for virtual hepatectomy.
  • The AI demonstrated high accuracy in dividing hemilivers, sectors, and segments, exceeding 90% for most structures.