SOFTooth: 2D-3D Fusion for Tooth Segmentation
Analysis
This paper addresses the challenges of 3D tooth instance segmentation, particularly in complex dental scenarios. It proposes a novel framework, SOFTooth, that leverages 2D semantic information from a foundation model (SAM) to improve 3D segmentation accuracy. The key innovation lies in fusing 2D semantics with 3D geometric information through a series of modules designed to refine boundaries, correct center drift, and maintain consistent tooth labeling, even in challenging cases. The results demonstrate state-of-the-art performance, especially for minority classes like third molars, highlighting the effectiveness of transferring 2D knowledge to 3D segmentation without explicit 2D supervision.
Key Takeaways
- •Proposes SOFTooth, a novel 2D-3D fusion framework for tooth instance segmentation.
- •Leverages 2D semantics from SAM to improve 3D segmentation accuracy.
- •Addresses challenges like crowded arches, ambiguous boundaries, and missing teeth.
- •Achieves state-of-the-art performance, especially for minority classes like third molars.
- •Demonstrates effective transfer of 2D knowledge to 3D segmentation without 2D fine-tuning.
“SOFTooth achieves state-of-the-art overall accuracy and mean IoU, with clear gains on cases involving third molars, demonstrating that rich 2D semantics can be effectively transferred to 3D tooth instance segmentation without 2D fine-tuning.”