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Related Experiment Video

Updated: May 10, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Couinaud segment-aware deep learning on point clouds for major liver resection planning.

Joy Rakshit1, Janine Rothert2, Georg Hille2

  • 1Institute for Medical Informatics and Statistics, University Hospital Schleswig-Holstein, Kiel, Germany. joy.rakshit@uksh.de.

International Journal of Computer Assisted Radiology and Surgery
|May 8, 2026
PubMed
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This study demonstrates that integrating Couinaud liver segments into deep learning models significantly enhances automatic liver resection planning. This anatomical information improves surgical precision for major liver procedures like hemi-hepatectomy.

Area of Science:

  • Medical Imaging
  • Surgical Planning
  • Deep Learning

Background:

  • Automatic liver resection planning is crucial for major surgeries like hemi-hepatectomy.
  • Couinaud liver segments are vital for anatomical description and surgical decision-making in liver procedures.
  • Current deep learning models may not fully leverage this anatomical information.

Purpose of the Study:

  • To investigate the impact of incorporating Couinaud liver segment information on deep learning model performance for automatic liver resection planning.
  • To enhance the clinical relevance and accuracy of AI-driven surgical planning for major liver resections.

Main Methods:

  • A point cloud-based geometric deep learning approach using a modified RandLA-Net architecture was developed.
  • The model was trained and validated on internal (70 cases) and external (30 cases) hemi-hepatectomy datasets.
Keywords:
Couinaud segmentsCouinaud-aware liver resection planningGeometric deep learningMajor hepatectomy planning using deep learningPoint cloud-based learning

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Last Updated: May 10, 2026

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  • Two composite loss functions (CE+IoU and CE+Dice) were evaluated with and without Couinaud segment data.

    • Main Results:

    • The CE+IoU loss function generally outperformed CE+Dice.
    • Incorporating Couinaud segment information significantly improved mean IoU (e.g., from 0.787 to 0.804 on the internal set) and F1-score.
    • Statistical analysis confirmed significant performance gains, particularly in preserving critical vascular structures.

    Conclusions:

    • Explicitly integrating Couinaud segment information enhances quantitative performance in automatic liver resection planning.
    • This approach improves clinical relevance by better preserving vital anatomical structures during simulated resections.
    • Deep learning models incorporating anatomical data offer a promising tool for improving surgical outcomes in liver surgery.