Innovative Tooth Segmentation Using Hierarchical Features and Bidirectional Sequence Modeling

TL;DR

This paper presents a novel hierarchical feature encoder combined with bidirectional sequence modeling for improved tooth segmentation, addressing limitations of traditional methods and reducing computational costs.

Contribution

The paper introduces a three-stage hierarchical encoder with cross-scale feature fusion and bidirectional sequence modeling for efficient, accurate dental image segmentation.

Findings

Achieved 1.1% higher mIoU on OralVision dataset.

Outperformed existing methods in segmentation accuracy.

Reduced computational complexity compared to transformer-based models.

Abstract

Tooth image segmentation is a cornerstone of dental digitization. However, traditional image encoders relying on fixed-resolution feature maps often lead to discontinuous segmentation and poor discrimination between target regions and background, due to insufficient modeling of environmental and global context. Moreover, transformer-based self-attention introduces substantial computational overhead because of its quadratic complexity (O(n^2)), making it inefficient for high-resolution dental images. To address these challenges, we introduce a three-stage encoder with hierarchical feature representation to capture scale-adaptive information in dental images. By jointly leveraging low-level details and high-level semantics through cross-scale feature fusion, the model effectively preserves fine structural information while maintaining strong contextual awareness. Furthermore, a bidirectional sequence modeling strategy is incorporated to enhance global spatial context understanding without incurring high computational cost. We validate our method on two dental datasets, with experimental results demonstrating its superiority over existing approaches. On the OralVision dataset, our model achieves a 1.1% improvement in mean intersection over union (mIoU).