Detecting Dental Landmarks from Intraoral 3D Scans: the 3DTeethLand challenge
Abstrak
Teeth landmark detection is a critical task in modern clinical orthodontics. Their precise identification enables advanced diagnostics, facilitates personalized treatment strategies, and supports more effective monitoring of treatment progress in clinical dentistry. However, several significant challenges may arise due to the intricate geometry of individual teeth and the substantial variations observed across different individuals. To address these complexities, the development of advanced techniques, especially through the application of deep learning, is essential for the precise and reliable detection of 3D tooth landmarks. In this context, the 3DTeethLand challenge was held in collaboration with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2024, calling for algorithms focused on teeth landmark detection from intraoral 3D scans. This challenge introduced the first publicly available dataset for 3D teeth landmark detection, offering a valuable resource to assess the state-of-the-art methods in this task and encourage the community to provide methodological contributions towards the resolution of their problem with significant clinical implications.
Topik & Kata Kunci
Penulis (26)
Achraf Ben-Hamadou
Nour Neifar
Ahmed Rekik
Oussama Smaoui
Firas Bouzguenda
Sergi Pujades
Niels van Nistelrooij
Shankeeth Vinayahalingam
Kaibo Shi
Hairong Jin
Youyi Zheng
Tibor Kubík
Oldřich Kodym
Petr Šilling
Kateřina Trávníčková
Tomáš Mojžiš
Jan Matula
Jeffry Hartanto
Xiaoying Zhu
Kim-Ngan Nguyen
Tudor Dascalu
Huikai Wu
and Weijie Liu
Shaojie Zhuang
Guangshun Wei
Yuanfeng Zhou
Akses Cepat
- Tahun Terbit
- 2025
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓