# A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-Rays
## 2 Literature Review  
Deep learning can help dentists by automating the process of teeth segmentation. This saves time and reduces human error, allowing dentists to focus on more important tasks such as accurate diagnosis and treatment planning. Various methods have been developed for teeth segmentation. [PERSON] et al. [6] employed FCN in a U-Net setup. [PERSON] et al. [7] introduced TASANet with a two-stage approach involving contextual attention and segmentation based on attention maps. [PERSON] et al. [8] extended spatial pyramid pooling (SPP) to MSLPNet and introduced MS-SSIM loss. [PERSON] and [PERSON] [9] proposed LTPEDN, replacing LBC layers with LTP layers. [PERSON] et al. [10] proposed Teeth U-Net using a multi-scale feature aggregation attention block (MAB) and dilated hybrid self-attention block (DHAB) in the bottleneck layer for improved segmentation.  
[PERSON] et al. [11] are credited for being the pioneers who attempted teeth instance segmentation from panoramic X-ray images. They employed a set of 193 images for training and subsequently assessed their approach on 1224 images, achieving an F1-score of 88%. [PERSON] et al. [12] also used Mask R-CNN on the Tufts Dental Database [13], which consists of 1000 panoramic dental radiographs, including both deciduous and permanent teeth. Their classification accuracy and dice score were 98.4% and 87%, respectively, and they observed poor segmentation for the right mandibular third molar. [PERSON] et al. [5] explored Mask R-CNN, PANet, HTC, and ResNeSt for teeth segmentation and numbering. They observed the best result for PANet with 71.3% of mAP for segmentation. [PERSON] and [PERSON] [14] employed a two-step methodology in which they employed a U-Net to create a binary prediction, followed by morphological operations to label connecting elements. So, the full segmentation process is not deep learning-based. Their study utilized a limited dataset comprising 116 patients, with 11 images reserved for testing purposes. [PERSON] et al. [15] tried MobileNet, AdapNet, DenseNet, and SegNet for maxillary teeth and palatal rugae segmentation. Their dataset consists of 797 occlusal views from teeth photographs. They achieved the best performance from SegNet with 86.66% average mIoU. [PERSON] and [PERSON] introduced a bilateral symmetry-based augmentation technique for panoramic X-rays, achieving a Dice Similarity Coefficient (DSC) of 76.7% using TransUNet [16]. Similarly, [PERSON] and [PERSON] presented a new dataset comprising 107 panoramic X-ray images, reporting an F1-score of 63% using Mask-RCNN [17].  
The above-mentioned works primarily addressed teeth segmentation, with some including numbering, yet none tackled tooth orientation. However, the orientation of teeth in 2D panoramic X-ray images is significant because it can help dentists identify the position of the teeth and their roots. This paper presents a two-stage framework: initially, utilizing deep learning for precise teeth segmentation, enabling subsequent numbering based on  
Figure 1: Gap-solution-clinical implication framework. The figure highlights the lack of orientation analysis in existing studies, outlines our deep learning-based solution with PCA-guided orientation and OBB annotations, and summarizes the resulting clinical benefits.  
individual labels; subsequently, employing principal component analysis (PCA) [18] to establish tooth orientation.