# CEPHA29: Automatic Cephalometric Landmark Detection Challenge 2023

arXiv: https://arxiv.org/abs/2212.04808 · HTML: https://mlsanity.com/paper/2212.04808/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 39 references that are in the map.
- First posted on arXiv 9 Dec 2022.

## Builds on (most cited first, 3 of 3)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Cephalometric Landmark Detection by AttentiveFeature Pyramid Fusion and Regression-Voting](https://mlsanity.com/paper/1908.08841/) (2019)
- [An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms](https://mlsanity.com/paper/1906.07549/) (2019)

## Built on by (most cited first, 6 of 6)

- [Leveraging Point Transformers for Detecting Anatomical Landmarks in Digital Dentistry](https://mlsanity.com/paper/2504.11418/) (2025)
- [Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development](https://mlsanity.com/paper/2603.27460/) (2026)
- [HYATT-Net is Grand: A Hybrid Attention Network for Performant Anatomical Landmark Detection](https://mlsanity.com/paper/2412.06499/) (2024)
- [Tracing Like a Clinician: Anatomy-Guided Spatial Priors for Cephalometric Landmark Detection](https://mlsanity.com/paper/2605.03358/) (2026)
- [Automatic Cephalometric Landmark Localization on CBCT-Derived Digitally Reconstructed Radiographs for Skeletal Malocclusion Classification](https://mlsanity.com/paper/2608.16535/) (2026)
- [LM-PCVMNet: Pediatric Cervical Vertebral Maturation Analysis with Deep Fusion of Landmarks and Metadata](https://mlsanity.com/paper/2609.16033/) (2026)

## 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.