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

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jinhee Kim , Taesung Kim , Taewoo Kim , Dong-Wook Kim , Byungduk Ahn , Yoon-Ji Kim , In-Seok Song , Jaegul Choo
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引用次数: 0

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.
参与和改进:交互式关键点估计和定量颈椎分析用于骨龄评估
在儿童正畸治疗中,准确估计生长潜力对于制定有效的治疗策略至关重要。我们的研究旨在通过识别生长高峰和仅通过侧位头颅x线片分析颈椎形态来预测这种潜力。我们通过综合分析这些x线片的颈椎成熟(CVM)特征来实现这一目标。该方法为临床医生提供了一个可靠和有效的工具来确定正畸干预的最佳时机,最终提高患者的治疗效果。这种方法的一个关键方面是对颈椎关键点进行细致的注释,这是一项经常受到劳动密集型性质挑战的任务。为了缓解这一问题,我们引入了参与和改进网络(ARNet),这是一种用户交互的、基于深度学习的模型,旨在简化注释过程。ARNet的特点是交互引导的重新校准网络,它根据用户反馈自适应地重新校准图像特征,再加上一个形态感知的损失函数,保持关键点的结构一致性。这种新颖的方法大大减少了关键点识别的人工工作,从而提高了过程的效率和准确性。ARNet在各种数据集上进行了广泛的验证,显示出卓越的性能,并在医学成像中表现出广泛的适用性。总之,我们的研究为评估儿童正畸生长潜力提供了一种有效的人工智能辅助诊断工具,标志着该领域的重大进步。
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
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