Geometric-Guided Few-Shot Dental Landmark Detection with Human-Centric Foundation Model
Journal:
arXiv
Published Date:
Jul 7, 2025
Abstract
Accurate detection of anatomic landmarks is essential for assessing alveolar
bone and root conditions, thereby optimizing clinical outcomes in orthodontics,
periodontics, and implant dentistry. Manual annotation of landmarks on
cone-beam computed tomography (CBCT) by dentists is time-consuming,
labor-intensive, and subject to inter-observer variability. Deep learning-based
automated methods present a promising approach to streamline this process
efficiently. However, the scarcity of training data and the high cost of expert
annotations hinder the adoption of conventional deep learning techniques. To
overcome these challenges, we introduce GeoSapiens, a novel few-shot learning
framework designed for robust dental landmark detection using limited annotated
CBCT of anterior teeth. Our GeoSapiens framework comprises two key components:
(1) a robust baseline adapted from Sapiens, a foundational model that has
achieved state-of-the-art performance in human-centric vision tasks, and (2) a
novel geometric loss function that improves the model's capacity to capture
critical geometric relationships among anatomical structures. Experiments
conducted on our collected dataset of anterior teeth landmarks revealed that
GeoSapiens surpassed existing landmark detection methods, outperforming the
leading approach by an 8.18% higher success detection rate at a strict 0.5 mm
threshold-a standard widely recognized in dental diagnostics. Code is available
at: https://github.com/xmed-lab/GeoSapiens.
