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基于特征的多层编码解码网络,用于对3D口腔扫描数据进行细分

Tian Ma¹, Xiaoyuan Wei², Jiechen Zhai¹

¹College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an, 710054, Shaanxi, China.

Scientific reports

|September 1, 2025

概括

这项研究引入了一种新的3D牙科模型细分方法,用于改善牙科缺陷分析. 这种新方法提高了复杂牙结构的细分精度和稳定性, 以更好地进行虚拟治疗计划.

科学领域:

计算机视觉
医学成像
牙整形

背景情况:

准确的缺陷细分对于正牙诊断和治疗计划至关重要.
现有的深度学习方法难以稳定,并且在缺陷细分方面存在混.
这限制了当前临床应用的可靠性.

研究的目的:

开发一个强大而准确的3D牙科模型细分方法.
提高深度学习在临床牙科应用中的可靠性.
提高牙缺陷的语义识别.

主要方法:

一个U形的3D细分网络,具有层次的特征指导.
一个功能引导的深度编码器,具有新的规范化方法和点云优化的推拉策略.
一个倒置的瓶全球特征提取流程和层次解码,用于高分辨率的网格重建.

主要成果:

在定制的形牙数据集上实现了96.6%的整体准确度 (OA) 和90.8%的平均交叉度 (mIoU).
显著超过现有的方法,
在公共数据集 (Teeth3DS,3D-IOSSeg) 上表现出强的表现,OA高达96.4%,mIoU高达94.5%.

结论:

与现有方法相比,该方法在缺陷细分方面提供了更好的性能.
它为智能虚拟牙科提供了强大而准确的解决方案.
该方法增强了高分辨率牙网的语义识别和空间特征重建.

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