论文标题
S-R2F2U-NET：牙齿分割的单阶段模型
S-R2F2U-Net: A single-stage model for teeth segmentation
论文作者
论文摘要
精密牙齿分割在口腔部门至关重要，因为它为正畸治疗，临床诊断和手术治疗提供了位置信息。在本文中，我们研究了剩余，经常性和注意力网络，以从全景牙科图像分割牙齿。根据我们的发现，我们建议三个单阶段模型：单复发R2U-NET（S-R2U-NET），单复发滤波器双R2U-NET（S-R2F2U-NET），以及启用了单个复发注意力的滤波器Double（S-R2F2-ATTN-U-NET）。特别是，S-R2F2U-NET就准确性和骰子得分而言优于最先进的模型。结合横向渗透损失和骰子损失的混合损失函数用于训练模型。此外，与R2U-NET模型相比，它减少了大约45％的模型参数。在包含1500个牙科全景X射线图像的基准数据集上训练和评估模型。 S-R2F2U-NET可获得精度的97.31％，掷骰子得分的93.26％，显示出优于最新方法的优势。代码可在https://github.com/mrinal054/teethseg_sr2f2u-net.git上找到。
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.
