Abstract
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 this https URL.
Abstract (translated)
颅面标志从侧位X光片的本地化是一个关键的颅面测量分析任务。因此,在过去的几十年里,相应任务的自动化已成为研究的热点。在本文中,我们引入了“颅面标志检测(CL-Detection)”数据集,这是目前公开可用的最大且全面的颅面标志检测数据集。这个多中心、多供应商的数据集包括来自三个医疗中心的600个侧位X光图像,其中有38个标志物使用了不同的设备进行测量。本文的总体目标是测量最先进的深度学习方法在颅面标志检测方面的进展。在2023年MICCAI CL-Detection挑战中,我们报道了使用深度学习方法排名前十的研究组的成果。结果表明,最好的方法与专家分析的准确度非常接近,实现了平均检测率为75.719%和平均径向误差为1.518毫米。虽然仍有很多改进的空间,但这些发现无疑为准确且完全自动地定位颅面标志打开了大门。我们还指出了深度学习方法仍然失败的场景。该数据集和详细结果都可以公开获取,而平台将始终保持开放,以供社区在此处对未来的算法发展进行基准测试。
URL
https://arxiv.org/abs/2409.15834