Attend-and-Refine: Interactive keypoint estimation and quantitative cervical vertebrae analysis for bone age assessment

TL;DR

This paper introduces ARNet, an interactive deep learning model that streamlines cervical vertebrae keypoint annotation from radiographs, improving efficiency and accuracy for pediatric growth assessment in orthodontics.

Contribution

The paper presents ARNet, a novel user-interactive network with feedback-guided recalibration and morphology-aware loss for efficient vertebral keypoint annotation.

Findings

ARNet significantly reduces manual annotation effort.

ARNet achieves high accuracy across diverse datasets.

The approach enhances clinical decision-making in orthodontics.

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.