mlsanityData to 7 Oct 2026

CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume Segmentation

arXiv 2208.01643 ↗First posted 2 Aug 2022eess.IV cs.AI cs.CVcited by 11

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Abstract (arXiv)

Accurate tooth volume segmentation is a prerequisite for computer-aided dental analysis. Deep learning-based tooth segmentation methods have achieved satisfying performances but require a large quantity of tooth data with ground truth. The dental data publicly available is limited meaning the existing methods can not be reproduced, evaluated and applied in clinical practice. In this paper, we establish a 3D dental CBCT dataset CTooth+, with 22 fully annotated volumes and 146 unlabeled volumes. We further evaluate several state-of-the-art tooth volume segmentation strategies based on fully-supervised learning, semi-supervised learning and active learning, and define the performance principles. This work provides a new benchmark for the tooth volume segmentation task, and the experiment can serve as the baseline for future AI-based dental imaging research and clinical application development.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 16 of its 28 references that are in the map. First posted on arXiv 2 Aug 2022.

Builds on

16 of 28 references.

References · 16

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4 more
  1. 2018Automatic segmenting teeth in X-ray images: Trends, a novel data set, benchmarking and future perspectivescited by 17
  2. 2022CTooth: A Fully Annotated 3D Dataset and Benchmark for Tooth Volume Segmentation on Cone Beam Computed Tomography Imagescited by 11
  3. 2021AGMB-Transformer: Anatomy-Guided Multi-Branch Transformer Network for Automated Evaluation of Root Canal Therapycited by 10
  4. 20173D Densely Convolutional Networks for Volumetric Segmentationcited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 4; 2025: 4; 2026: 1.

11 papers.

  1. 2024Stable Diffusion Segmentation for Biomedical Images with Single-step Reverse Processcited by 16
  2. 2025MedSG-Bench: A Benchmark for Medical Image Sequences Groundingcited by 8
  3. 2024STS MICCAI 2023 Challenge: Grand challenge on 2D and 3D semi-supervised tooth segmentationcited by 3
  4. 20233D-U-SAM Network For Few-shot Tooth Segmentation in CBCT Imagescited by 2
  5. 2024Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Anthropic Prior Knowledgecited by 2
  6. 2025MICCAI STSR 2025 Challenge: Semi-Supervised Teeth and Pulp Segmentation and CBCT-IOS Registrationcited by 2
  7. 2023AI Techniques for Cone Beam Computed Tomography in Dentistry: Trends and Practicescited by 0
  8. 2024A Multi-Stage Framework for 3D Individual Tooth Segmentation in Dental CBCTcited by 0
  9. 2025Cross-Frequency Collaborative Training Network and Dataset for Semi-supervised First Molar Root Canal Segmentationcited by 0
  10. 2025U-Mamba2-SSL for Semi-Supervised Tooth and Pulp Segmentation in CBCTcited by 0
  11. 2026Teeth2Point: A Two-Stage Dental CBCT ROI-to-Point Segmentation Frameworkcited by 0