# Fully automatic integration of dental CBCT images and full-arch intraoral impressions with stitching error correction via individual tooth segmentation and identification

arXiv: https://arxiv.org/abs/2112.01784 · HTML: https://mlsanity.com/paper/2112.01784/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 45 references that are in the map.
- First posted on arXiv 3 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [A fully automated method for 3D individual tooth identification and segmentation in dental CBCT](https://mlsanity.com/paper/2102.06060/) (2021)
- [Automatic Registration between Cone-Beam CT and Scanned Surface via Deep-Pose Regression Neural Networks and Clustered Similarities](https://mlsanity.com/paper/1907.12250/) (2019)

## Built on by (most cited first, 5 of 5)

- [Nonlinear ill-posed problem in low-dose dental cone-beam computed tomography](https://mlsanity.com/paper/2303.01678/) (2023)
- [Automatic 3D Registration of Dental CBCT and Face Scan Data using 2D Projection Images](https://mlsanity.com/paper/2305.10132/) (2023)
- [PRAD: Periapical Radiograph Analysis Dataset and Benchmark Model Development](https://mlsanity.com/paper/2504.07760/) (2025)
- [Cross-Frequency Collaborative Training Network and Dataset for Semi-supervised First Molar Root Canal Segmentation](https://mlsanity.com/paper/2504.11856/) (2025)
- [APCReg: Anatomical-Prior-Guided Coarse-to-Fine CBCT--IOS Registration via Multi-View Projection and Reliability-Controlled Residual Correction](https://mlsanity.com/paper/2608.09993/) (2026)

## Abstract (arXiv)

We present a fully automated method of integrating intraoral scan (IOS) and dental cone-beam computerized tomography (CBCT) images into one image by complementing each image's weaknesses. Dental CBCT alone may not be able to delineate precise details of the tooth surface due to limited image resolution and various CBCT artifacts, including metal-induced artifacts. IOS is very accurate for the scanning of narrow areas, but it produces cumulative stitching errors during full-arch scanning. The proposed method is intended not only to compensate the low-quality of CBCT-derived tooth surfaces with IOS, but also to correct the cumulative stitching errors of IOS across the entire dental arch. Moreover, the integration provide both gingival structure of IOS and tooth roots of CBCT in one image. The proposed fully automated method consists of four parts; (i) individual tooth segmentation and identification module for IOS data (TSIM-IOS); (ii) individual tooth segmentation and identification module for CBCT data (TSIM-CBCT); (iii) global-to-local tooth registration between IOS and CBCT; and (iv) stitching error correction of full-arch IOS. The experimental results show that the proposed method achieved landmark and surface distance errors of 112.4 μm and 301.7 μm, respectively.