Precision tooth segmentation is crucial in the oral sector because it provides location information for orthodontic therapy, clinical diagnosis, and surgical treatments. In this paper, we investigate residual, recurrent, and attention networks to segment teeth from panoramic dental images. Based on our findings, we suggest three single-stage models: Single Recurrent R2U-Net (S-R2U-Net), Single Recurrent Filter Double R2U-Net (S-R2F2U-Net), and Single Recurrent Attention Enabled Filter Double (S-R2F2-Attn-U-Net). Particularly, S-R2F2U-Net outperforms state-of-the-art models in terms of accuracy and dice score. A hybrid loss function combining the cross-entropy loss and dice loss is used to train the model. In addition, it reduces around 45% of model parameters compared to the R2U-Net model. Models are trained and evaluated on a benchmark dataset containing 1500 dental panoramic X-ray images. S-R2F2U-Net achieves 97.31% of accuracy and 93.26% of dice score, showing superiority over the state-of-the-art methods. Codes are available at https://github.com/mrinal054/teethSeg_sr2f2u-net.git.

翻译：精准的牙齿分割在口腔领域至关重要，因为它为正畸治疗、临床诊断和外科手术提供了位置信息。本文研究了残差网络、循环网络和注意力网络在牙科全景图像中分割牙齿的应用。基于研究结果，我们提出了三种单阶段模型：单循环R2U-Net（S-R2U-Net）、单循环滤波双R2U-Net（S-R2F2U-Net）和单循环注意力增强双滤波U-Net（S-R2F2-Attn-U-Net）。其中，S-R2F2U-Net在准确率和Dice系数方面均优于现有最优模型。模型采用交叉熵损失与Dice损失相结合的混合损失函数进行训练。此外，与R2U-Net模型相比，该模型减少了约45%的参数。模型在包含1500张牙科全景X光图像的基准数据集上进行训练和评估。S-R2F2U-Net达到了97.31%的准确率和93.26%的Dice系数，展现出优于现有最先进方法的性能。代码可在https://github.com/mrinal054/teethSeg_sr2f2u-net.git获取。