Pith. sign in

REVIEW 2 cited by

3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2305.18277 v1 pith:K7VI6OOK submitted 2023-05-29 cs.CV cs.AI

Achraf Ben-Hamadou , Oussama Smaoui , Ahmed Rekik , Sergi Pujades , Edmond Boyer , Hoyeon Lim , Minchang Kim , Minkyung Lee
show 24 more authors
Minyoung Chung Yeong-Gil Shin Mathieu Leclercq Lucia Cevidanes Juan Carlos Prieto Shaojie Zhuang Guangshun Wei Zhiming Cui Yuanfeng Zhou Tudor Dascalu Bulat Ibragimov Tae-Hoon Yong Hong-Gi Ahn Wan Kim Jae-Hwan Han Byungsun Choi Niels van Nistelrooij Steven Kempers Shankeeth Vinayahalingam Julien Strippoli Aurélien Thollot Hugo Setbon Cyril Trosset Edouard Ladroit
This is my paper · ORCID
classification cs.CVcs.AI
keywords challengedteethsegteethalgorithmslabelingscanssegmentationchallenges
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Teeth localization, segmentation, and labeling from intra-oral 3D scans are essential tasks in modern dentistry to enhance dental diagnostics, treatment planning, and population-based studies on oral health. However, developing automated algorithms for teeth analysis presents significant challenges due to variations in dental anatomy, imaging protocols, and limited availability of publicly accessible data. To address these challenges, the 3DTeethSeg'22 challenge was organized in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2022, with a call for algorithms tackling teeth localization, segmentation, and labeling from intraoral 3D scans. A dataset comprising a total of 1800 scans from 900 patients was prepared, and each tooth was individually annotated by a human-machine hybrid algorithm. A total of 6 algorithms were evaluated on this dataset. In this study, we present the evaluation results of the 3DTeethSeg'22 challenge. The 3DTeethSeg'22 challenge code can be accessed at: https://github.com/abenhamadou/3DTeethSeg22_challenge

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DentiAsk: A VQA Benchmark for Multimodal Reasoning in Panoramic Dental Radiographs

    q-bio.QM 2026-06 conditional novelty 6.0 of 10

    A 1,000-image, 10,000-QA dental VQA benchmark shows current VLMs handle descriptive recognition far better than spatial localization or numerical counting on panoramic radiographs.

  2. Topology-Constrained Quantized nnUNet for Efficient and Anatomically Accurate 3D Tooth Segmentation

    cs.CV 2026-05 unverdicted novelty 5.0 of 10

    A topology-constrained 8-bit quantized nnUNet with connected-component, adjacency, and hole penalties reduces topological errors in tooth segmentation compared to standard quantization.

Pith tools