Zhao Ling, Huang Juneng, Tang Min, Zhang Xuejun, Xiao Lijuan, Tao Renchuan
College of Stomatology, Hospital of Stomatology, Guangxi Medical University, Nanning, Guangxi, China.
School of Computer, Electronics and Information, Guangxi University, Nanning, China.
J Imaging Inform Med. 2025 Feb 25. doi: 10.1007/s10278-025-01447-0.
The objective of the study is to establish a novel method for automatic cephalometric superimposition on the basis of feature matching and compare it with the commonly used Sella-Nasion (SN) superimposition method. A total of 178 pairs of pre- (T1) and post-treatment (T2) lateral cephalometric radiographs (LCRs) from adult orthodontic patients were collected. Ninety LCR pairs were used to train the you only look once version 8 (YOLOv8) model to automatically recognize stable cranial reference areas. This approach represents a novel method for automated superimposition on the basis of feature matching. The remaining 88 LCR pairs were used for landmark identification by three orthodontic experts to evaluate the accuracy of the two superimposition methods. The Euclidean distances of 17 hard tissue landmarks were measured and statistically compared after superimposition. Significant differences were observed in the superimposition error of most landmarks between the two methods (p < 0.05). The successful detection rate (SDR) of automatic superimposition of each landmark within the precision ranges of 1 mm, 2 mm, and 3 mm via the new method was higher than that via the SN method. The new automatic superimposition method is more accurate than the SN method and is a reliable method for superimposing adult LCRs, thus providing support for clinical or research work.
本研究的目的是基于特征匹配建立一种新的自动头影测量叠加方法，并将其与常用的蝶鞍-鼻根点（SN）叠加方法进行比较。收集了178对来自成年正畸患者的治疗前（T1）和治疗后（T2）的头颅侧位X线片（LCR）。90对LCR用于训练你只看一次版本8（YOLOv8）模型以自动识别稳定的颅骨参考区域。这种方法代表了一种基于特征匹配的自动叠加新方法。其余88对LCR由三位正畸专家用于标志点识别，以评估两种叠加方法的准确性。在叠加后测量并统计比较17个硬组织标志点的欧几里得距离。两种方法之间大多数标志点的叠加误差存在显著差异（p < 0.05）。通过新方法在1毫米、2毫米和3毫米精度范围内每个标志点的自动叠加成功检测率（SDR）高于SN方法。新的自动叠加方法比SN方法更准确，是一种可靠的成年LCR叠加方法，从而为临床或研究工作提供支持。
