CLIK-Diffusion:临床知识为基础的牙齿排列扩散模型

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yulong Dou , Han Wu , Changjian Li , Chen Wang , Tong Yang , Min Zhu , Dinggang Shen , Zhiming Cui
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引用次数: 0

摘要

传统的半自动牙齿矫正方法涉及繁琐的人工程序,严重依赖牙医的专业知识,这往往导致效率低下和治疗时间延长。虽然已经提出了许多自动化方法来帮助特别是经验不足的牙医,但它们往往缺乏结合临床洞察力,并且通过直接从牙齿点云估计每个牙齿的刚性变换矩阵来过度简化问题。这种过度简化未能捕捉到正畸治疗的细微要求,即有效对齐错位牙齿的具体临床规则。为了解决这个问题,我们提出了CLIK-Diffusion,这是一种用于自动牙齿对齐的临床知识扩散模型。CLIK-Diffusion将复杂的牙齿对准问题表述为一个更易于管理的地标转换问题,并进一步细化为地标坐标生成任务。具体来说,我们首先按类别检测每个牙齿的地标,然后构建我们的CLIK-Diffusion来学习正常咬合的分布。为了进一步促进临床基本知识的整合,我们从三个角度设计了分层约束:(1)牙弓层面:从全局层面约束牙齿的排列;(2)齿间水平:保证紧密接触,避免相邻齿间不必要的碰撞;(3)单齿水平:保证每颗牙齿的正确定位。通过这种方式,我们设计的clickk - diffusion能够预测与临床知识一致的正畸后标志,然后根据每颗牙齿正畸前和后标志的坐标估计其刚性转变。我们已经在实际临床收集的各种错颌病例中评估了我们的CLIK-Diffusion,并与其他最先进的方法相比,证明了它在正畸治疗中的卓越性能和强大适用性。我们的数据集和代码可在https://github.com/ShanghaiTech-IMPACT/CLIK-Diffusion上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CLIK-Diffusion: Clinical Knowledge-informed Diffusion Model for Tooth Alignment
Traditional semi-automatic methods for tooth alignment involve laborious manual procedures and heavily depend on the expertise of dentists, which often leads to inefficient and prolonged treatment durations. Although many automatic methods have been proposed to assist especially the less experienced dentists, they often lack incorporating clinical insight and oversimplify the problem by estimating rigid transformation matrix for each tooth directly from dental point clouds. This over-simplification fails to capture nuanced requirements of orthodontic treatment, i.e., specific clinical rules for effective alignment of misaligned teeth. To address this, we propose CLIK-Diffusion, a CLInical Knowledge-informed Diffusion model for automatic tooth alignment. CLIK-Diffusion formulates the complex problem of tooth alignment as a more manageable landmark transformation problem, which is further refined into a landmark coordinate generation task. Specifically, we first detect landmarks for each tooth by category, and then build our CLIK-Diffusion to learn distribution of normal occlusion. To further encourage the integration of essential clinical knowledge, we design hierarchical constraints from three perspectives: (1) dental-arch level: to constrain arrangement of teeth from a global level; (2) inter-tooth level: to ensure tight contact and avoid unnecessary collision between neighboring teeth; and (3) individual-tooth level: to guarantee correct orientation of each tooth. In this way, our designed CLIK-Diffusion is able to predict the post-orthodontic landmarks that align with clinical knowledge, and then estimate rigid transformation for each tooth based on coordinates of its pre- and post-orthodontic landmarks. We have evaluated our CLIK-Diffusion on various malocclusion cases collected in real-world clinics, and demonstrate its exceptional performance and strong applicability in orthodontic treatment, compared with other state-of-the-art methods. Our dataset and code is available at https://github.com/ShanghaiTech-IMPACT/CLIK-Diffusion.
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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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