Bi-level alignment with super-resolution head for unsupervised cephalometric landmark localization

Objective. Cephalometric alandmark 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 (UDA) setting.Approach. In this paper, we propose bi-Level alignment with super-resolution head, an effective framework for precise and robust anatomical landmark detection under UDA. 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 UDA methods, highlighting its potential for clinical applications in cephalometric analysis and orthodontic surgery planning.