  Accurate teeth segmentation and orientation are fundamental in modern oral
healthcare, enabling precise diagnosis, treatment planning, and dental implant
design. In this study, we present a comprehensive approach to teeth
segmentation and orientation from panoramic X-ray images, leveraging deep
learning techniques. We build our model based on FUSegNet, a popular model
originally developed for wound segmentation, and introduce modifications by
incorporating grid-based attention gates into the skip connections. We
introduce oriented bounding box (OBB) generation through principal component
analysis (PCA) for precise tooth orientation estimation. Evaluating our
approach on the publicly available DNS dataset, comprising 543 panoramic X-ray
images, we achieve the highest Intersection-over-Union (IoU) score of 82.43%
and Dice Similarity Coefficient (DSC) score of 90.37% among compared models in
teeth instance segmentation. In OBB analysis, we obtain the Rotated IoU (RIoU)
score of 82.82%. We also conduct detailed analyses of individual tooth labels
and categorical performance, shedding light on strengths and weaknesses. The
proposed model's accuracy and versatility offer promising prospects for
improving dental diagnoses, treatment planning, and personalized healthcare in
the oral domain. Our generated OBB coordinates and codes are available at
https://github.com/mrinal054/Instance_teeth_segmentation.
