  This paper proposed a cutting-edge multiclass teeth segmentation architecture
that integrates an M-Net-like structure with Swin Transformers and a novel
component named Teeth Attention Block (TAB). Existing teeth image segmentation
methods have issues with less accurate and unreliable segmentation outcomes due
to the complex and varying morphology of teeth, although teeth segmentation in
dental panoramic images is essential for dental disease diagnosis. We propose a
novel teeth segmentation model incorporating an M-Net-like structure with Swin
Transformers and TAB. The proposed TAB utilizes a unique attention mechanism
that focuses specifically on the complex structures of teeth. The attention
mechanism in TAB precisely highlights key elements of teeth features in
panoramic images, resulting in more accurate segmentation outcomes. The
proposed architecture effectively captures local and global contextual
information, accurately defining each tooth and its surrounding structures.
Furthermore, we employ a multiscale supervision strategy, which leverages the
left and right legs of the U-Net structure, boosting the performance of the
segmentation with enhanced feature representation. The squared Dice loss is
utilized to tackle the class imbalance issue, ensuring accurate segmentation
across all classes. The proposed method was validated on a panoramic teeth
X-ray dataset, which was taken in a real-world dental diagnosis. The
experimental results demonstrate the efficacy of our proposed architecture for
tooth segmentation on multiple benchmark dental image datasets, outperforming
existing state-of-the-art methods in objective metrics and visual examinations.
This study has the potential to significantly enhance dental image analysis and
contribute to advances in dental applications.
