The annotation of three-dimensional (3D) cephalometric landmarks in 3D computerized tomography (CT) has become an essential part of cephalometric analysis, which is used for diagnosis, surgical planning, and treatment evaluation. The automation of 3D landmarking with high-precision remains challenging due to the limited availability of training data and the high computational burden. This paper addresses these challenges by proposing a hierarchical deep-learning method consisting of four stages: 1) a basic landmark annotator for 3D skull pose normalization, 2) a deep-learning-based coarse-to-fine landmark annotator on the midsagittal plane, 3) a low-dimensional representation of the total number of landmarks using variational autoencoder (VAE), and 4) a local-to-global landmark annotator. The implementation of the VAE allows two-dimensional-image-based 3D morphological feature learning and similarity/dissimilarity representation learning of the concatenated vectors of cephalometric landmarks. The proposed method achieves an average 3D point-to-point error of 3.63 mm for 93 cephalometric landmarks using a small number of training CT datasets. Notably, the VAE captures variations of craniofacial structural characteristics.

中文翻译：

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基于学习的局部到全局界标注释，用于自动3D头颅测量。

3D电脑断层扫描（CT）中的三维（3D）头颅标志性标记已成为头颅分析的重要组成部分，用于诊断，手术计划和治疗评估。由于训练数据的有限可用性和高计算量，高精度的3D地标自动化仍然具有挑战性。本文提出了一种由四个阶段组成的分层深度学习方法来应对这些挑战：1）3D头骨姿势归一化的基本界标注释器； 2）矢状面中基于深度学习的粗到细界标注释器； 3）使用变分自动编码器（VAE）进行地标总数的低维表示，以及4）本地到全局地标注释器。VAE的实现可实现基于二维图像的3D形态学特征学习和头部测量学界标的连接向量的相似性/相异性表示学习。所提出的方法使用少量训练CT数据集，对93个头颅地标实现了3.63 mm的平均3D点对点误差。值得注意的是，VAE捕获了颅面结构特征的变化。