# CTooth: A Fully Annotated 3D Dataset and Benchmark for Tooth Volume Segmentation on Cone Beam Computed Tomography Images

arXiv: https://arxiv.org/abs/2206.08778 · HTML: https://mlsanity.com/paper/2206.08778/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 17 Jun 2022.

## Builds on (most cited first, 16 of 16)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [Selective Kernel Networks](https://mlsanity.com/paper/1903.06586/) (2019)
- [Polarized Self-Attention: Towards High-quality Pixel-wise Regression](https://mlsanity.com/paper/2107.00782/) (2021)
- [On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task](https://mlsanity.com/paper/1707.01992/) (2017)
- [Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets](https://mlsanity.com/paper/1708.00573/) (2017)
- [Automatic segmenting teeth in X-ray images: Trends, a novel data set, benchmarking and future perspectives](https://mlsanity.com/paper/1802.03086/) (2018)

## Built on by (most cited first, 11 of 11)

- [Stable Diffusion Segmentation for Biomedical Images with Single-step Reverse Process](https://mlsanity.com/paper/2406.18361/) (2024)
- [CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume Segmentation](https://mlsanity.com/paper/2208.01643/) (2022)
- [MedSG-Bench: A Benchmark for Medical Image Sequences Grounding](https://mlsanity.com/paper/2505.11852/) (2025)
- [3D-U-SAM Network For Few-shot Tooth Segmentation in CBCT Images](https://mlsanity.com/paper/2309.11015/) (2023)
- [Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Anthropic Prior Knowledge](https://mlsanity.com/paper/2404.01013/) (2024)
- [Spatial-Frequency Dual Progressive Attention Network For Medical Image Segmentation](https://mlsanity.com/paper/2406.07952/) (2024)
- [RegFreeNet: A Registration-Free Network for CBCT-based 3D Dental Implant Planning](https://mlsanity.com/paper/2601.14703/) (2026)
- [RAIL: Region-Aware Instructive Learning for Semi-Supervised Tooth Segmentation in CBCT](https://mlsanity.com/paper/2505.03538/) (2025)
- [AI Techniques for Cone Beam Computed Tomography in Dentistry: Trends and Practices](https://mlsanity.com/paper/2306.03025/) (2023)
- [A Multi-Stage Framework for 3D Individual Tooth Segmentation in Dental CBCT](https://mlsanity.com/paper/2407.10433/) (2024)
- [Adaptive Texture-aware Masking for Self-Supervised Learning in 3D Dental CBCT Analysis](https://mlsanity.com/paper/2605.01741/) (2026)

## Abstract (arXiv)

3D tooth segmentation is a prerequisite for computer-aided dental diagnosis and treatment. However, segmenting all tooth regions manually is subjective and time-consuming. Recently, deep learning-based segmentation methods produce convincing results and reduce manual annotation efforts, but it requires a large quantity of ground truth for training. To our knowledge, there are few tooth data available for the 3D segmentation study. In this paper, we establish a fully annotated cone beam computed tomography dataset CTooth with tooth gold standard. This dataset contains 22 volumes (7363 slices) with fine tooth labels annotated by experienced radiographic interpreters. To ensure a relative even data sampling distribution, data variance is included in the CTooth including missing teeth and dental restoration. Several state-of-the-art segmentation methods are evaluated on this dataset. Afterwards, we further summarise and apply a series of 3D attention-based Unet variants for segmenting tooth volumes. This work provides a new benchmark for the tooth volume segmentation task. Experimental evidence proves that attention modules of the 3D UNet structure boost responses in tooth areas and inhibit the influence of background and noise. The best performance is achieved by 3D Unet with SKNet attention module, of 88.04 % Dice and 78.71 % IOU, respectively. The attention-based Unet framework outperforms other state-of-the-art methods on the CTooth dataset. The codebase and dataset are released.