Robust automatic hexahedral cartilage meshing framework enables population-based computational studies of the knee

Area of Science:

• Computational biomechanics
• Medical imaging analysis
• Deep learning applications

Background:

• Knee osteoarthritis prevalence is rising, increasing healthcare costs and impacting quality of life.
• Current computational methods for knee biomechanics are limited by invasive procedures and costly cadaveric studies.
• Generating hexahedral meshes for finite element analysis is a bottleneck for large-scale knee biomechanical investigations.

Purpose of the Study:

• To develop a fully automated pipeline for generating finite element simulations of the knee from MRI data.
• To overcome the limitations of manual meshing in computational knee biomechanics.
• To enable population-sized investigations of knee joint mechanics.

Main Methods:

• An encoder-decoder convolutional neural network was trained for semantic image segmentation using the Osteoarthritis Initiative dataset.
• An open-source swept-extrusion meshing algorithm was enhanced for automated, high-quality hexahedral mesh generation.
• A template-mapping procedure was implemented for automatic soft-tissue attachment point placement.

Main Results:

• The automated pipeline successfully generated simulation-ready meshes for 176 knee MRI sequences in under 2 minutes per knee.
• 86% of meshes from provided segmentations completed simulated flexion-extension.
• Automated reconstructions showed mean root-mean-squared differences under 20% compared to manual reconstructions in tibiofemoral contact mechanics.

Conclusions:

• This automated framework significantly reduces the time and effort required for finite element model generation.
• The pipeline enables feasible, population-sized finite element studies of natural knee biomechanics.
• This approach holds promise for advancing osteoarthritis research and understanding knee joint function.