Clinical Scorecard: Deep Learning Utilizing Couinaud Segmentation on Point Clouds for Planning Major Liver Resections

At a Glance

Key Highlights

• Liver resection is critical for treating primary and metastatic liver cancers.
• Couinaud segmentation provides a standard framework for liver anatomy.
• Deep learning models can enhance preoperative planning accuracy.
• Integration of point clouds and meshes supports immersive surgical visualization.
• Automatic resection prediction frameworks assist in surgical decision-making.
• Preserving future liver remnant (FLR) is crucial for postoperative liver function.

Guideline-Based Recommendations

Diagnosis

• Utilize imaging techniques to assess tumor extent and vascular anatomy.

Management

• Employ Couinaud segmentation for planning liver resections, ensuring adequate safety margins.

Monitoring & Follow-up

• Regularly evaluate liver function and future liver remnant post-surgery.

Risks

• Consider risks related to incomplete tumor removal and inadequate future liver remnant.

Patient & Prescribing Data

Preoperative planning must ensure adequate safety margins and functional liver preservation, considering specific tumor types.

Clinical Best Practices

• Incorporate 3D visualization tools for enhanced surgical planning.
• Utilize AI-assisted planning to reduce radiological workload.
• Ensure comprehensive assessment of vascular structures during preoperative planning.
• Integrate AI-assisted tools for real-time surgical decision-making.

References

• Couinaud Segmentation
• Deep Learning in Liver Surgery