BATISNet: Instance Segmentation for Tooth Point Clouds
Analysis
This paper addresses the challenge of accurate tooth segmentation in dental point clouds, a crucial task for clinical applications. It highlights the limitations of semantic segmentation in complex cases and proposes BATISNet, a boundary-aware instance segmentation network. The focus on instance segmentation and a boundary-aware loss function are key innovations to improve accuracy and robustness, especially in scenarios with missing or malposed teeth. The paper's significance lies in its potential to provide more reliable and detailed data for clinical diagnosis and treatment planning.
Key Takeaways
- •Proposes BATISNet, a boundary-aware instance segmentation network for tooth point clouds.
- •Addresses limitations of semantic segmentation in complex dental cases.
- •Employs instance segmentation and a boundary-aware loss function for improved accuracy.
- •Focuses on handling issues like missing and malposed teeth.
- •Demonstrates superior performance compared to existing methods.
“BATISNet outperforms existing methods in tooth integrity segmentation, providing more reliable and detailed data support for practical clinical applications.”