Cartilage Repair Based on Point Cloud Completion

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Abstract
In recent years, owing to advancements in medical imaging technology, the three-dimensional reconstruction of cartilage structures has become increasingly important in medical image processing. However, the repair of cartilage loss is still manually operated by medical experts. In order to address this challenge and achieve automatic cartilage repair, this paper constructs a femur dataset and proposes a method called SAPoinTr for cartilage point cloud completion. The method introduces the Inverted Residual MLP (InvResMLP) module and a multi-branch network to enhance feature extraction capability. This algorithm ensures the density of the generated surface, avoiding holes or redundant meshes that traditional methods may produce. Experimental results demonstrate our approach exhibits notable enhancements in completing cartilage point clouds. The generated point cloud not only matches the real data in geometric shape, but also exhibits excellent consistency and smoothness.
Funding Information
  • National Natural Science Foundation of China
  • Research and Innovation Foundation

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