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Related Experiment Video

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Automated Volumetric Milling Area Planning for Acoustic Neuroma Surgery via Evolutionary Multi-Objective

Sheng Yang1, Haowei Li1, Peihai Zhang2

  • 1School of Biomedical Engineering, Tsinghua University, Shuang Qing Road, Beijing 100084, China.

Sensors (Basel, Switzerland)
|January 25, 2025
PubMed
Summary

This study introduces an automated method for planning mastoidectomy bone milling using evolutionary optimization. The approach enhances surgical safety and efficiency by reducing potential damage and improving access during acoustic neuroma surgery.

Keywords:
bone millingmulti-objective optimizationvolumetric area parameterizationvolumetric surgical planning

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Area of Science:

  • Neurosurgery
  • Medical Imaging
  • Computational Optimization

Background:

  • Mastoidectomy is crucial for acoustic neuroma surgery, requiring precise bone milling plans.
  • Irregular volumetric areas and proximity to critical structures complicate manual planning.
  • Automated planning is needed for safer and more efficient surgical navigation.

Purpose of the Study:

  • To develop an automated method for planning the bone milling area in mastoidectomy using preoperative CT images.
  • To optimize milling plans for enhanced safety and efficiency in acoustic neuroma surgery.
  • To parameterize irregular volumetric regions for automated planning.

Main Methods:

  • High-resolution segmentation of risk structures using a template-based approach on CT images.
  • Definition of maximum milling area based on risk structure constraints and tool dimensions.
  • Application of deformation fields and evolutionary multi-objective optimization for Pareto-optimal design.

Main Results:

  • Reduced potential damage to the scala vestibuli by 29.8% compared to manual planning.
  • Improved milling boundary smoothness by 78.3%.
  • Increased target accessibility by 26.4%.

Conclusions:

  • The proposed parameterization and optimization approach enables automated, safe, and feasible milling area planning.
  • The method demonstrated significant improvements in safety and efficiency metrics.
  • The technique is adaptable for various volumetric planning scenarios in surgery.