用分层特征与双向序列建模，提升牙科图像分割精度与效率
Innovative Tooth Segmentation Using Hierarchical Features and Bidirectional Sequence Modeling
- 分三阶段编码器融合多尺度特征，兼顾细节与上下文
- 在OralVision数据集上mIoU提升1.1%，优于现有方法
- 双向序列建模降低计算开销，适合高分辨率牙科图像
牙科图像分割是牙科数字化的基础。传统基于固定分辨率特征图的编码器常因环境与全局上下文建模不足，导致分割不连续且目标与背景区分能力差。此外，基于Transformer的自注意力机制因二次复杂度（O(n²)）带来巨大计算开销，难以高效处理高分辨率牙科图像。为此，我们提出一种三阶段编码器，通过分层特征表示捕获牙科图像中的尺度自适应信息。通过跨尺度特征融合，联合利用低层细节与高层语义，有效保留精细结构信息的同时保持强上下文感知能力。此外，引入双向序列建模策略，在不增加高计算成本的前提下增强全局空间上下文理解。我们在两个牙科数据集上验证了该方法，实验结果表明其优于现有方法：在OralVision数据集上，平均交并比（mIoU）提升1.1%。
原文摘要 · Abstract (English)
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).
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