Deep learning-based automatic cranial implant design through direct defect shape prediction and its comparison study

Area of Science:

• Medical Engineering
• Artificial Intelligence in Medicine
• Neurosurgery

Background:

• Cranial defects, resulting from trauma or surgery, necessitate reconstructive procedures using cranial implants.
• The manual design of patient-specific cranial implants is a time-consuming process, impacting overall therapy duration.
• Automating implant design is essential for improving efficiency and patient outcomes in reconstructive surgery.

Purpose of the Study:

• To propose and evaluate an automated workflow for cranial implant design.
• To leverage deep neural networks for direct shape prediction of missing cranial segments.
• To refine the automated design process through post-processing steps and assess its clinical applicability.

Main Methods:

• A deep neural network was developed for direct shape prediction of defective cranial areas.
• Conventional post-processing techniques were applied to refine the generated implant shapes.
• Cross-validation was used to evaluate the accuracy of the automated design.
• A plugin for 3D Slicer was created to implement the workflow for end-users.

Main Results:

• The automated workflow achieved an average Dice Similarity Score of 0.81 and a boundary Dice Similarity Score of 0.81.
• The 95th quantile of the Hausdorff Distance averaged 3.01 mm, indicating good surface accuracy.
• The proposed method demonstrated convenience and efficiency compared to manual cranial implant design.

Conclusions:

• The developed deep learning-based workflow effectively automates cranial implant design.
• The automated system provides accurate implant shape prediction and refinement.
• The 3D Slicer plugin facilitates the adoption and use of this technology by clinicians and researchers.