mlsanityData to 7 Oct 2026

CEPHA29: Automatic Cephalometric Landmark Detection Challenge 2023

arXiv 2212.04808 ↗First posted 9 Dec 2022cs.CVcited by 5

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Abstract (arXiv)

Quantitative cephalometric analysis is the most widely used clinical and research tool in modern orthodontics. Accurate localization of cephalometric landmarks enables the quantification and classification of anatomical abnormalities, however, the traditional manual way of marking these landmarks is a very tedious job. Endeavours have constantly been made to develop automated cephalometric landmark detection systems but they are inadequate for orthodontic applications. The fundamental reason for this is that the amount of publicly available datasets as well as the images provided for training in these datasets are insufficient for an AI model to perform well. To facilitate the development of robust AI solutions for morphometric analysis, we organise the CEPHA29 Automatic Cephalometric Landmark Detection Challenge in conjunction with IEEE International Symposium on Biomedical Imaging (ISBI 2023). In this context, we provide the largest known publicly available dataset, consisting of 1000 cephalometric X-ray images. We hope that our challenge will not only derive forward research and innovation in automatic cephalometric landmark identification but will also signal the beginning of a new era in the discipline.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 3 of its 39 references that are in the map. First posted on arXiv 9 Dec 2022.

Builds on

3 of 39 references.

References · 3

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2019Cephalometric Landmark Detection by AttentiveFeature Pyramid Fusion and Regression-Votingcited by 32
  3. 2019An Attention-Guided Deep Regression Model for Landmark Detection in Cephalogramscited by 27

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 3.

5 papers.