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Updated: Jan 13, 2026
Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
Published on: September 8, 2023
Alineación de dos niveles con cabeza de superresolución para la localización de puntos de referencia cefalométricos
Gang Lu1, Xiangwen Wang1, Mangang Xie1
1College of Computer Science and Engineering, Northwest Normal University, No. 967 Anning East Road, Lanzhou, Gansu, 730070, CHINA.
Objective:
Cephalometric landmark localization is of great clinical significance in diagnosing and treating patients with dental-maxillofacial deformities. Domain shifts across clinical centers significantly hinder model generalizability, causing existing methods to struggle with accurate and robust anatomical landmark localization due to insufficient alignment of high-level semantic features across domains. We aim to improve the cross-domain generalizability of cephalometric landmark detection by aligning semantic features and enhancing output resolution under an unsupervised domain adaptation setting.
Approach:
In this paper, we propose Bi-Level Alignment with Super-Resolution head (BiLASR), an effective framework for precise and robust anatomical landmark detection under unsupervised domain adaptation. Specifically, we employ adaptive instance normalization to generate target-style images while preserving original anatomical spatial structure at the input level. At the output level, a Mean-Teacher framework leverages high-quality pseudo-labels from the teacher model to guide the student model's learning. Additionally, a lightweight super-resolution head enables the generation of high-resolution heatmaps from the multi-scale feature maps and the low-resolution heatmaps, reducing quantization errors with low computational cost.
Results:
The proposed method achieved a mean localization error of 1.64 mm, a successful detection rate of 72.68% within the clinically acceptable threshold of 2 mm, and an average classification accuracy of 81.81% for anatomical types.
Significance:
Extensive experiments on public cephalometric datasets demonstrate superiority over state-of-the-art unsupervised domain adaptation methods, highlighting its potential for clinical applications in cephalometric analysis and orthodontic surgery planning.
