Attend-and-Refine: Interactive keypoint estimation and quantitative cervical vertebrae analysis for bone age assessment
Journal:
arXiv
Published Date:
Jul 10, 2025
Abstract
In pediatric orthodontics, accurate estimation of growth potential is
essential for developing effective treatment strategies. Our research aims to
predict this potential by identifying the growth peak and analyzing cervical
vertebra morphology solely through lateral cephalometric radiographs. We
accomplish this by comprehensively analyzing cervical vertebral maturation
(CVM) features from these radiographs. This methodology provides clinicians
with a reliable and efficient tool to determine the optimal timings for
orthodontic interventions, ultimately enhancing patient outcomes. A crucial
aspect of this approach is the meticulous annotation of keypoints on the
cervical vertebrae, a task often challenged by its labor-intensive nature. To
mitigate this, we introduce Attend-and-Refine Network (ARNet), a
user-interactive, deep learning-based model designed to streamline the
annotation process. ARNet features Interaction-guided recalibration network,
which adaptively recalibrates image features in response to user feedback,
coupled with a morphology-aware loss function that preserves the structural
consistency of keypoints. This novel approach substantially reduces manual
effort in keypoint identification, thereby enhancing the efficiency and
accuracy of the process. Extensively validated across various datasets, ARNet
demonstrates remarkable performance and exhibits wide-ranging applicability in
medical imaging. In conclusion, our research offers an effective AI-assisted
diagnostic tool for assessing growth potential in pediatric orthodontics,
marking a significant advancement in the field.
