Deep Learning Techniques for Automatic Lateral X-ray Cephalometric Landmark Detection: Is the Problem Solved?
<|reference_start|>Deep Learning Techniques for Automatic Lateral X-ray Cephalometric Landmark Detection: Is the Problem Solved?: Localization of the craniofacial landmarks from lateral cephalograms is a fundamental task in cephalometric analysis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Cephalometric Landmark Detection (CL-Detection)" dataset, which is the largest publicly available and comprehensive dataset for cephalometric landmark detection. This multi-center and multi-vendor dataset includes 600 lateral X-ray images with 38 landmarks acquired with different equipment from three medical centers. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go for cephalometric landmark detection. Following the 2023 MICCAI CL-Detection Challenge, we report the results of the top ten research groups using deep learning methods. Results show that the best methods closely approximate the expert analysis, achieving a mean detection rate of 75.719% and a mean radial error of 1.518 mm. While there is room for improvement, these findings undeniably open the door to highly accurate and fully automatic location of craniofacial landmarks. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for the community to benchmark future algorithm developments at https://cl-detection2023.grand-challenge.org/.<|reference_end|>
@article{zhang2024deep,
    title={Deep Learning Techniques for Automatic Lateral X-ray Cephalometric
  Landmark Detection: Is the Problem Solved?},
    author={Hongyuan Zhang, Ching-Wei Wang, Hikam Muzakky, Juan Dai, Xuguang Li,
  Chenglong Ma, Qian Wu, Xianan Cui, Kunlun Xu, Pengfei He, Dongqian Guo,
  Xianlong Wang, Hyunseok Lee, Zhangnan Zhong, Zhu Zhu and Bingsheng Huang},
    journal={arXiv preprint arXiv:2409.15834},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15834},
    primaryClass={cs.CV}
}