%0 English Abstract
%T [Research on intelligent tooth segmentation method combining multiple seed region growth and boundary extension].
%A Liu Z
%A Xue J
%A Tang H
%A Liao Y
%J Sheng Wu Yi Xue Gong Cheng Xue Za Zhi
%V 41
%N 3
%D 2024 Jun 25
%M 38932538
暂无%R 10.7507/1001-5515.202309030
%X The segmentation of dental models is a crucial step in computer-aided diagnosis and treatment systems for oral healthcare. To address the issues of poor universality and under-segmentation in tooth segmentation techniques, an intelligent tooth segmentation method combining multiple seed region growth and boundary extension is proposed. This method utilized the distribution characteristics of negative curvature meshes in teeth to obtain new seed points and effectively adapted to the structural differences between the top and sides of teeth through differential region growth. Additionally, the boundaries of the initial segmentation were extended based on geometric features, which was effectively compensated for under-segmentation issues in region growth. Ablation experiments and comparative experiments with current state-of-the-art algorithms demonstrated that the proposed method achieved better segmentation of crowded dental models and exhibited strong algorithm universality, thus possessing the capability to meet the practical segmentation needs in oral healthcare.<BR>三维牙颌模型的分割是计算机口腔辅助诊疗系统的关键步骤。针对牙齿分割技术普适性差、欠分割的问题，提出一种结合多种子区域生长和边界延伸的智能牙齿分割方法。该方法结合牙齿的负曲率网格的分布特点获得新种子点，通过差异性区域生长适应了牙齿顶面和侧面间的差异性结构，并结合几何特征对初次分割的边界进行延伸，有效弥补了区域生长的欠分割缺陷。在实验中，通过与当前经典的算法进行对比和消融实验，表明本文方法能够更好地分割拥挤的牙颌模型，具有较强算法普适性，已具备满足口腔诊疗中实际分割需求的能力。.