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相关概念视频

Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Imaging Studies III: Computed Tomography01:27

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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相关实验视频

Updated: May 6, 2026

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
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A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

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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
PubMed
概括
此摘要是机器生成的。

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.

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相关实验视频

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Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
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Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

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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.