# A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-Rays
## 1 Introduction  
Accurate teeth segmentation is important in oral healthcare. It provides location data for orthodontic treatments, clinical diagnoses, and surgical procedures. It is also used to identify individuals, capture tooth morphology, and plan dental implants [1]. Manually segmenting teeth is time-consuming and challenging, even for experienced professionals. Semi-automatic techniques can help, but they still require some human input. This is especially true for lower-resolution images, with which it can be difficult to accurately delineate certain tooth regions. Teeth segmentation from panoramic X-ray images can be of two types: semantic segmentation and instance segmentation. Semantic segmentation is a simple approach to dental analysis that labels the entire teeth region with a single label. This simplicity makes it computationally efficient and easy to implement. Semantic segmentation is well suited for general dental analysis for which distinguishing individual teeth is not essential, such as identifying overall dental health or disease patterns. Semantic segmentationcould be used to track the progression of dental diseases over time. This would allow dentists to monitor the effectiveness of treatment and make adjustments as needed. Teeth instance segmentation assigns separate labels to each individual tooth, enabling precise identification of each tooth's boundaries and characteristics. With instance segmentation, dental professionals can accurately analyze the health of each tooth, aiding in personalized treatment planning for orthodontic procedures and other dental interventions. A dentist can use instance segmentation to identify a tooth that is decayed. This information can be used to plan the best course of treatment, such as a filling or a root canal. Instance segmentation allows for detailed quantitative analysis, such as measuring gaps between teeth, assessing wear patterns, and monitoring specific dental issues on a per-tooth basis. For tasks like designing dental prosthetics or implants, instance segmentation helps in creating accurate models that fit each tooth precisely. While 3D instance segmentation is particularly valuable for complex cases that require a thorough understanding of tooth morphology, such as orthognathic surgery planning, dental implant placement, and in-depth orthodontic assessments, 2D instance segmentation is well suited for routine dental analyses and treatments that can be effectively assessed from a two-dimensional perspective, such as cavity detection, treatment planning, and basic orthodontic evaluations. Moreover, 2D instance segmentation is generally faster and requires less computational resources compared to its 3D counterpart, making it suitable for tasks that demand quick results. In this paper, we use panoramic dental X-ray images for instance segmentation. Panoramic X-ray images are widely used for applications like dental caries, alveolar bone resorption, and impacted teeth [2]. Research works on panoramic X-ray images are mostly limited to teeth detection, teeth segmentation, and teeth numbering. Orientations of teeth are not well explored. It can help dentists and oral surgeons diagnose dental problems and plan treatments, particularly for procedures related to restorative dentistry, such as dental implants, teeth alignment, and orthodontic interventions. Typically, positioning technology and segmentation are conducted in separate frameworks. Consequently, the development cycle takes longer, and the algorithm's complexity is elevated [3]. Our contribution to this paper can be summarized as follows:  
* We propose a two-stage framework that returns individual segmented teeth and their orientations. We adopt a deep learning-based approach to segment individual teeth, followed by applying Principal Component Analysis (PCA) to determine tooth orientation. Such outcomes can facilitate obtaining precise teeth positions in an image.