CEPHA29: Automatic Cephalometric Landmark Detection Challenge 2023
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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
Built on by
Citing papers per year: 2024: 1; 2025: 1; 2026: 3.
5 papers.
- 2025Leveraging Point Transformers for Detecting Anatomical Landmarks in Digital Dentistrycited by 2
- 2024HYATT-Net is Grand: A Hybrid Attention Network for Performant Anatomical Landmark Detectioncited by 1
- 2026Tracing Like a Clinician: Anatomy-Guided Spatial Priors for Cephalometric Landmark Detectioncited by 0
- 2026Automatic Cephalometric Landmark Localization on CBCT-Derived Digitally Reconstructed Radiographs for Skeletal Malocclusion Classificationcited by 0
- 2026LM-PCVMNet: Pediatric Cervical Vertebral Maturation Analysis with Deep Fusion of Landmarks and Metadatacited by 0
- 2026LM-PCVMNet: Pediatric Cervical Vertebral Maturation Analysis with Deep Fusion of Landmarks and Metadata11 Sep 2026
- 2026Automatic Cephalometric Landmark Localization on CBCT-Derived Digitally Reconstructed Radiographs for Skeletal Malocclusion Classification17 Aug 2026
- 2026Tracing Like a Clinician: Anatomy-Guided Spatial Priors for Cephalometric Landmark Detection5 May 2026
- 2025Leveraging Point Transformers for Detecting Anatomical Landmarks in Digital Dentistry15 Apr 2025
- 2024HYATT-Net is Grand: A Hybrid Attention Network for Performant Anatomical Landmark Detection9 Dec 2024