# A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-Rays
## 5 Conclusions  
In this paper, we address the critical tasks of teeth segmentation and orientation in the context of oral healthcare. Accurate teeth segmentation holds immense importance for various applications, including orthodontic treatments, clinical diagnoses, surgical procedures, and dental implant planning. We recognize the challenges posed by manual segmentation, especially in lower-resolution images, and the limitations of existing semi-automatic techniques that still require human intervention. Our model architecture includes an EfficientNet-based encoder and a decoder with grid-based attention gates and a parallel spatial and channel squeeze-and-excitation (P-scSE) module. We also introduce oriented bounding boxes (OBB) generation using principal component analysis (PCA). By addressing tooth orientation, a largely unexplored problem, within the same framework as segmentation, our work extends beyond current methodologies, advancing the field toward more comprehensive and clinically useful analysis tools. Our model's accuracy and capabilities open new possibilities for improving dental diagnoses and treatment planning, even when working with clinically standard, moderate-resolution panoramic X-rays. This makes the approach practical for real-world deployment where ultra-high-resolution imaging is not always available. Future works include extending our approach to 3D instance segmentation of teeth, which is crucial for applications like orthognathic surgery planning and dental implant placement, would be a valuable direction. In addition, we plan to explore advanced data augmentation techniques, including geometric transformations, elastic deformations, and intensity perturbations, to improve the robustness and generalization of the model. We also aim to adapt the proposed framework to other dental imaging modalities, such as bitewing and periapical X-rays, with appropriate adjustments for their localized views and anatomical characteristics.  
M.D. ([PERSON]) and M.K.D. defined the project scope, methodology, design, and overall purpose of the study. M.D. ([PERSON]) and M.D. ([PERSON]) developed the models, conducted testing and validation, and contributed to technical writing. M.D. ([PERSON]) performed the literature review. M.D. ([PERSON]) and M.D. ([PERSON]) drafted the manuscript. [PERSON] handled proofreading and manuscript organization. [PERSON] conceptualized the oriented bounding box. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Restrictions apply to the availability of these data. Data were obtained from the authors of \"[PERSON], [PERSON], et al. A study on tooth segmentation and numbering using end-to-end deep neural networks. 2020 33 rd SIBGRAPH conference on graphics, patterns and images (SIBGRAPI). IEEE, 2020, and are available from them with permission. The codes are available at [[https://github.com/mrinal054/Instance_teeth_segmentation](https://github.com/mrinal054/Instance_teeth_segmentation)]([https://github.com/mrinal054/Instance_teeth_segmentation](https://github.com/mrinal054/Instance_teeth_segmentation)) (accessed on 6 August 2025).  
**Conflicts of Interest:** The authors declare no conflicts of interest.