# A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-rays

arXiv: https://arxiv.org/abs/2310.17176 · HTML: https://mlsanity.com/paper/2310.17176/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 38 references that are in the map.
- First posted on arXiv 26 Oct 2023.

## Builds on (most cited first, 19 of 19)

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- [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation](https://mlsanity.com/paper/1707.03718/) (2017)
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- [FUSegNet: A Deep Convolutional Neural Network for Foot Ulcer Segmentation](https://mlsanity.com/paper/2305.02961/) (2023)
- [Self-Supervised Learning with Masked Image Modeling for Teeth Numbering, Detection of Dental Restorations, and Instance Segmentation in Dental Panoramic Radiographs](https://mlsanity.com/paper/2210.11404/) (2022)
- [Tooth Instance Segmentation on Panoramic Dental Radiographs Using U-Nets and Morphological Processing](https://mlsanity.com/paper/2204.00095/) (2022)
- [S-R2F2U-Net: A single-stage model for teeth segmentation](https://mlsanity.com/paper/2204.02939/) (2022)

## Abstract (arXiv)

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 built an end-to-end instance segmentation network that uses an encoder-decoder architecture reinforced with grid-aware attention gates along 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 a Dice Similarity Coefficient (DSC) score of 90.37% among compared models in teeth instance segmentation. In OBB analysis, we obtain a 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 code are available at https://github.com/mrinal054/Instance/teeth/segmentation.