# Automatic segmenting teeth in X-ray images: Trends, a novel data set, benchmarking and future perspectives

arXiv: https://arxiv.org/abs/1802.03086 · HTML: https://mlsanity.com/paper/1802.03086/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2018.

## Built on by (most cited first, 17 of 17)

- [CTooth: A Fully Annotated 3D Dataset and Benchmark for Tooth Volume Segmentation on Cone Beam Computed Tomography Images](https://mlsanity.com/paper/2206.08778/) (2022)
- [CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume Segmentation](https://mlsanity.com/paper/2208.01643/) (2022)
- [Weakly Supervised Few-Shot Segmentation Via Meta-Learning](https://mlsanity.com/paper/2109.01693/) (2021)
- [FluoroSAM: A Language-promptable Foundation Model for Flexible X-ray Image Segmentation](https://mlsanity.com/paper/2403.08059/) (2024)
- [Learning to Segment Medical Images from Few-Shot Sparse Labels](https://mlsanity.com/paper/2108.05476/) (2021)
- [PanoGAN A Deep Generative Model for Panoramic Dental Radiographs](https://mlsanity.com/paper/2507.21200/) (2025)
- [Tooth Instance Segmentation on Panoramic Dental Radiographs Using U-Nets and Morphological Processing](https://mlsanity.com/paper/2204.00095/) (2022)
- [Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Anthropic Prior Knowledge](https://mlsanity.com/paper/2404.01013/) (2024)
- [OralBBNet: Spatially Guided Dental Segmentation of Panoramic X-Rays with Bounding Box Priors](https://mlsanity.com/paper/2406.03747/) (2024)
- [DeepOPG: Improving Orthopantomogram Finding Summarization with Weak Supervision](https://mlsanity.com/paper/2103.08290/) (2021)
- [OdontoAI: A human-in-the-loop labeled data set and an online platform to boost research on dental panoramic radiographs](https://mlsanity.com/paper/2203.15856/) (2022)
- [S-R2F2U-Net: A single-stage model for teeth segmentation](https://mlsanity.com/paper/2204.02939/) (2022)
- [Exploring the Role of Convolutional Neural Networks (CNN) in Dental Radiography Segmentation: A Comprehensive Systematic Literature Review](https://mlsanity.com/paper/2401.09190/) (2024)
- [Artificial Intelligence to Assess Dental Findings from Panoramic Radiographs -- A Multinational Study](https://mlsanity.com/paper/2502.10277/) (2025)
- [A tomographic workflow to enable deep learning for X-ray based foreign object detection](https://mlsanity.com/paper/2201.12184/) (2022)
- [Semi-supervised classification of dental conditions in panoramic radiographs using large language model and instance segmentation: A real-world dataset evaluation](https://mlsanity.com/paper/2406.17915/) (2024)
- [Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking](https://mlsanity.com/paper/2505.15637/) (2025)

## Abstract (arXiv)

This review presents an in-depth study of the literature on segmentation methods applied in dental imaging. Ten segmentation methods were studied and categorized according to the type of the segmentation method (region-based, threshold-based, cluster-based, boundary-based or watershed-based), type of X-ray images used (intra-oral or extra-oral) and characteristics of the dataset used to evaluate the methods in the state-of-the-art works. We found that the literature has primarily focused on threshold-based segmentation methods (54%). 80% of the reviewed papers have used intra-oral X-ray images in their experiments, demonstrating preference to perform segmentation on images of already isolated parts of the teeth, rather than using extra-oral X-rays, which show tooth structure of the mouth and bones of the face. To fill a scientific gap in the field, a novel data set based on extra-oral X-ray images are proposed here. A statistical comparison of the results found with the 10 image segmentation methods over our proposed data set comprised of 1,500 images is also carried out, providing a more comprehensive source of performance assessment. Discussion on limitations of the methods conceived over the past year as well as future perspectives on exploiting learning-based segmentation methods to improve performance are also provided.