Teeth3DS+: An Extended Benchmark for Intraoral 3D Scans Analysis

The challenge 3DTeethSeg22 is a first edition associated with MICCAI 2022. It is organized by Udini (France) in collaboration with Inria Grenoble Morpheo team (France) and the Digital Research Center of Sfax (Tunisia).

Data description

A total of 1800 3D intra-oral scans have been collected for 900 patients covering their upper and lower jaws separately. The ground truth tooth labels and tooth instances for each vertex in the obj files are provided in JavaScript Object Notation (JSON) format. A JSON file example is shown below:

{
    "id_patient": "6X24ILNE",
    "jaw": "upper",
    "labels": [0, 0, 44, 33, 34, 0, 0, 45, 0, .. ,41,  0, 0, 37, 0, 34, 45, 0, 31, 36],
    "instances": [0, 0, 10, 2, 12, 0, 0, 9, 0, 0, .. , 10, 0, 0, 8, 0, 0, 9, 0, 1, 8, 13],
}

The length of the tables "labels" and "instances" is the same as the total number of vertices in the corresponding 3D scan. The label and instance ”0” are reserved by default for gingiva. And, other than ”0”, the unique numbers in table ”instances” indicate the number of teeth in the 3D scan. The labels are provided in the FDI numbering system.

Download

Dataset is structured under 6 data parts. It is required to download all of them and merge them to a same folder architecture. url : https://osf.io/xctdy/

Dataset splits

Two dataset train/test splits are provided , which specify the samples to consider for each dataset: 3D Teeth Seg Challenge split (used during the challenge) Teeth3DS official dataset split

Evaluation metrics

• Teeth localization accuracy (TLA): calculated as the mean of normalized Euclidean distance between ground truth (GT) teeth centroids and the closest localized teeth centroid. Each computed Euclidean distance is normalized by the size of the corresponding GT tooth. In case of no centroid (e.g. algorithm crashes or missing output for a given scan) a nominal penalty of 5 per GT tooth will be given. This corresponds to a distance 5 times the actual GT tooth size. As the number of teeth per patient may be variable, here the mean is computed over all gathered GT Teeth in the two testing sets.
• Teeth identification rate (TIR): is computed as the percentage of true identification cases relatively to all GT teeth in the two testing sets. A true identification is considered when for a given GT Tooth, the closest detected tooth centroid : is localized at a distance under half of the GT tooth size, and is attributed the same label as the GT tooth
• Teeth segmentation accuracy (TSA): is computed as the average F1-score over all instances of teeth point clouds. The F1-score of each tooth instance is measured as: F1=2*(precision * recall)/(precision+recall)

Leaderboard

Citing us

@article{ben2022teeth3ds,
title={{Teeth3Ds+: An Extended Benchmark for Intra-oral 3D Scans Analysis}},
author={Ben-Hamadou, Achraf and Neifar, Nour and Rekik, Ahmed and Smaoui, Oussama and Bouzguenda, Firas and Pujades, Sergi and  Boyer, Edmond and Ladroit, Edouard},
journal={arXiv preprint arXiv:2210.06094},
year={2022}
}

@article{ben20233dteethseg,
title={3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge},
author={Achraf Ben-Hamadou and Oussama Smaoui and Ahmed Rekik and Sergi Pujades and Edmond Boyer and Hoyeon Lim and Minchang Kim and Minkyung Lee and Minyoung Chung and Yeong-Gil Shin and Mathieu Leclercq and Lucia Cevidanes and Juan Carlos Prieto and Shaojie Zhuang and Guangshun Wei and Zhiming Cui and Yuanfeng Zhou and Tudor Dascalu and Bulat Ibragimov and Tae-Hoon Yong and Hong-Gi Ahn and Wan Kim and Jae-Hwan Han and Byungsun Choi and Niels van Nistelrooij and Steven Kempers and Shankeeth Vinayahalingam and Julien Strippoli and Aurélien Thollot and Hugo Setbon and Cyril Trosset and Edouard Ladroit},
journal={arXiv preprint arXiv:2305.18277},
year={2023}
}