Department of Orthodontics, University of Adelaide, Adelaide, South Australia.
My Orthodontics Pty Ltd, Adelaide, South Australia.
Eur J Orthod. 2021 Dec 1;43(6):622-630. doi: 10.1093/ejo/cjaa069.
Due to technological advances, the quantification of facial form can now be done via three-dimensional (3D) photographic systems such as stereophotogrammetry. To enable comparison with traditional cephalometry, soft-tissue anatomical landmark definitions have been modified to incorporate the third dimension. Annotating these landmarks manually, however, is still a time-consuming and arduous process.
To develop an automated algorithm to accurately identify anatomical landmarks on three-dimensional soft tissue images.
Thirty 3dMD images were selected from a private orthodontic practice consisting of 15 males and 15 females between 9 and 17 years of age. The soft-tissue 3D images were aligned along a reference plane to setup a Cartesian coordinate system. Screened by 2 observers, 21 landmarks were manually annotated and their coordinates defined. An automated landmark identification algorithm, based on their anatomical definitions, was developed to compare the landmark validity against the manually identified counterpart.
Twenty-one landmarks were analysed in detail. Inter-observer and intra-observer reliability using ICC was >0.9. The average difference and standard deviation between manual and automated methods for all landmarks was 3.2 and 1.64 mm, respectively. Sixteen out of twenty-one landmarks had a mean difference less than 4 mm. The landmarks of greatest agreement (≤2 mm) were mainly in the midline: pronasale, subnasale, subspinale, labiale superius, stomion, with the exception of chelion right. Five linear facial measurements were found to have moderate to good agreement between the manual and automated identification methods.
The developed algorithm was determined to be clinically relevant in the detection of midsagittal landmarks and associated measurements within the studied sample of adolescent Caucasian subjects.
由于技术的进步，现在可以通过三维（3D）摄影系统（如体视摄影测量）来定量面部形态。为了能够与传统的头影测量法进行比较，已经对软组织解剖标志定义进行了修改，纳入了第三维。然而，手动标注这些标志仍然是一个耗时且费力的过程。
开发一种自动算法，以准确识别三维软组织图像上的解剖标志。
从一家私人正畸诊所中选择了 30 张 3dMD 图像，该诊所由 9 至 17 岁的 15 名男性和 15 名女性组成。将软组织 3D 图像沿着参考平面对齐，以建立笛卡尔坐标系。由 2 名观察者筛选，手动标注了 21 个标志并定义了它们的坐标。开发了一种基于其解剖定义的自动标志识别算法，以比较标志的有效性与手动识别的对应物。
详细分析了 21 个标志。使用 ICC 进行的观察者间和观察者内可靠性>0.9。所有标志的手动和自动方法之间的平均差异和标准差分别为 3.2 和 1.64mm。21 个标志中有 16 个的平均差异小于 4mm。标志差异最大（≤2mm）的主要是中线标志：鼻前点、鼻下点、下颏前点、上唇龈点、唇珠点，除了右侧切牙。在研究的白种青少年样本中，有 5 个线性面部测量值被发现手动和自动识别方法之间具有中等至良好的一致性。
在所研究的白种青少年样本中，开发的算法在检测正中标志和相关测量值方面被确定为具有临床相关性。
