CEPHA29: Automatic Cephalometric Landmark Detection Challenge 2023

• URL: http://arxiv.org/abs/2212.04808v2
• Date: Mon, 3 Apr 2023 10:27:21 GMT
• Title: CEPHA29: Automatic Cephalometric Landmark Detection Challenge 2023
• Authors: Muhammad Anwaar Khalid, Kanwal Zulfiqar, Ulfat Bashir, Areeba Shaheen, Rida Iqbal, Zarnab Rizwan, Ghina Rizwan, Muhammad Moazam Fraz
• Abstract summary: We organise the CEPHA29 Automatic Cephalometric Landmark Detection Challenge. We provide the largest known publicly available dataset, consisting of 1000 cephalometric X-ray images. We hope that our challenge will signal the beginning of a new era in the discipline.
• Score: 0.402058998065435
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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