# Pose-Aware Instance Segmentation Framework from Cone Beam CT Images for Tooth Segmentation

arXiv: https://arxiv.org/abs/2002.02143 · HTML: https://mlsanity.com/paper/2002.02143/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2020.

## Built on by (most cited first, 10 of 10)

- [SNAF: Sparse-view CBCT Reconstruction with Neural Attenuation Fields](https://mlsanity.com/paper/2211.17048/) (2022)
- [DentVLM: A Multimodal Vision-Language Model for Comprehensive Dental Diagnosis and Enhanced Clinical Practice](https://mlsanity.com/paper/2509.23344/) (2025)
- [Tooth Instance Segmentation on Panoramic Dental Radiographs Using U-Nets and Morphological Processing](https://mlsanity.com/paper/2204.00095/) (2022)
- [Cardiac Segmentation on CT Images through Shape-Aware Contour Attentions](https://mlsanity.com/paper/2105.13153/) (2021)
- [Teeth Localization and Lesion Segmentation in CBCT Images using SpatialConfiguration-Net and U-Net](https://mlsanity.com/paper/2312.12189/) (2023)
- [GEPAR3D: Geometry Prior-Assisted Learning for 3D Tooth Segmentation](https://mlsanity.com/paper/2508.00155/) (2025)
- [Tooth Instance Segmentation from Cone-Beam CT Images through Point-based Detection and Gaussian Disentanglement](https://mlsanity.com/paper/2102.01315/) (2021)
- [Convolutional Neural Networks in Orthodontics: a review](https://mlsanity.com/paper/2104.08886/) (2021)
- [An efficient method to automate tooth identification and 3D bounding box extraction from Cone Beam CT Images](https://mlsanity.com/paper/2407.05892/) (2024)
- [Shape-preserving Tooth Segmentation from CBCT Images Using Deep Learning with Semantic and Shape Awareness](https://mlsanity.com/paper/2511.16936/) (2025)

## Abstract (arXiv)

Individual tooth segmentation from cone beam computed tomography (CBCT) images is an essential prerequisite for an anatomical understanding of orthodontic structures in several applications, such as tooth reformation planning and implant guide simulations. However, the presence of severe metal artifacts in CBCT images hinders the accurate segmentation of each individual tooth. In this study, we propose a neural network for pixel-wise labeling to exploit an instance segmentation framework that is robust to metal artifacts. Our method comprises of three steps: 1) image cropping and realignment by pose regressions, 2) metal-robust individual tooth detection, and 3) segmentation. We first extract the alignment information of the patient by pose regression neural networks to attain a volume-of-interest (VOI) region and realign the input image, which reduces the inter-overlapping area between tooth bounding boxes. Then, individual tooth regions are localized within a VOI realigned image using a convolutional detector. We improved the accuracy of the detector by employing non-maximum suppression and multiclass classification metrics in the region proposal network. Finally, we apply a convolutional neural network (CNN) to perform individual tooth segmentation by converting the pixel-wise labeling task to a distance regression task. Metal-intensive image augmentation is also employed for a robust segmentation of metal artifacts. The result shows that our proposed method outperforms other state-of-the-art methods, especially for teeth with metal artifacts. The primary significance of the proposed method is two-fold: 1) an introduction of pose-aware VOI realignment followed by a robust tooth detection and 2) a metal-robust CNN framework for accurate tooth segmentation.