DE-KAN提升牙科全景片牙齿分割精度，解决重叠与形状不规则难题。
DE-KAN: A Kolmogorov Arnold Network with Dual Encoder for accurate 2D Teeth Segmentation
- 双编码器分别处理增强与原始图像，融合全局与局部特征
- 基于KAN的瓶颈层使模型在保持可解释性的同时提升性能
- 在两个数据集上达到97.1%的Dice系数，优于现有方法4.7%
由于解剖结构差异、牙齿形状不规则及重叠结构，从全景牙片中精确分割单个牙齿仍是挑战。为此，我们提出DE-KAN：一种新型双编码器柯尔莫哥洛夫-阿诺德网络，以增强特征表达与分割精度。该框架采用ResNet-18编码器处理增强输入，定制化CNN编码器处理原始输入，实现全局与局部空间特征的互补提取。特征通过基于KAN的瓶颈层融合，引入源自柯尔莫哥洛夫-阿诺德表示定理的非线性可学习激活函数，提升学习能力与可解释性。在两个基准牙科X光数据集上的大量实验表明，DE-KAN超越当前最优分割模型，在mIoU达94.5%、Dice系数97.1%、准确率98.91%、召回率97.36%的基础上，相比现有方法最高提升4.7%（以Dice计）。
原文摘要 · Abstract (English)
Accurate segmentation of individual teeth from panoramic radiographs remains a challenging task due to anatomical variations, irregular tooth shapes, and overlapping structures. These complexities often limit the performance of conventional deep learning models. To address this, we propose DE-KAN, a novel Dual Encoder Kolmogorov Arnold Network, which enhances feature representation and segmentation precision. The framework employs a ResNet-18 encoder for augmented inputs and a customized CNN encoder for original inputs, enabling the complementary extraction of global and local spatial features. These features are fused through KAN-based bottleneck layers, incorporating nonlinear learnable activation functions derived from the Kolmogorov Arnold representation theorem to improve learning capacity and interpretability. Extensive experiments on two benchmark dental X-ray datasets demonstrate that DE-KAN outperforms state-of-the-art segmentation models, achieving mIoU of 94.5%, Dice coefficient of 97.1%, accuracy of 98.91%, and recall of 97.36%, representing up to +4.7% improvement in Dice compared to existing methods.
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