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相关概念视频

Computed Tomography01:10

Computed Tomography

7.9K
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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Longitudinal changes in epigenetic clocks predict survival in the InCHIANTI cohort.

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Compatibility and comparative analysis of chronological and biological aging between the legacy 450K and the EPIC v2.0 arrays.

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LiftReg: Limited Angle 2D/3D Deformable Registration.

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Inverse Consistency by Construction for Multistep Deep Registration.

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Can Crowdsourced Annotations Improve AI-based Congestion Scoring For Bedside Lung Ultrasound?

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Equivariant Filters for Efficient Tracking in 3D Imaging.

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Lobar Lung Density Embeddings with a Transformer encoder (LobTe) to predict emphysema progression in COPD.

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相关实验视频

Updated: Jan 7, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

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通过深度条件生成模型减少腹部CT截面切片的位置变异.

Xin Yu1, Qi Yang1, Yucheng Tang2

  • 1Computer Science, Vanderbilt University, Nashville, TN, USA.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|December 26, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了C-SliceGen,这是一种新的方法,可以从纵向计算机断层扫描 (CT) 扫描中协调身体组成分析. 它减少了腹部切片的位置变异,改善了健康和衰老研究.

关键词:
腹部切片的生成过程身体组成 身体组成纵向数据协调与统一

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
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Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

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相关实验视频

Last Updated: Jan 7, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 生物医学工程 生物医学工程

背景情况:

  • 2D腹部CT切片可以测量身体成分,这对于老化健康研究至关重要.
  • 纵向分析受到CT切片随时间变化的位置差异的阻碍.
  • 标准化这些切片对于准确的身体成分跟踪至关重要.

研究的目的:

  • 开发一种方法 (C-SliceGen) 来减少纵向2D腹部CT切片中的位置偏差.
  • 为了使更准确的身体组成分析随着时间的推移进行衰老研究.
  • 为了使不同脊椎水平的切片与目标切片协调.

主要方法:

  • 扩展条件生成模型以创建C-SliceGen.
  • 模型将任意的腹部切片作为输入,并生成定义的脊椎水平切片.
  • 估计潜空间的结构变化,以解释位置变异.

主要成果:

  • C-SliceGen产生高质量,现实的和类似的图像.
  • 在内部 (1170名受试者) 和BTCV MICCAI (50名受试者) 数据集上验证.
  • 在BLSA数据集 (20名受试者) 中协调肌肉和内脏脂肪区域的纵向切片.

结论:

  • C-SliceGen有效地减少了纵向腹部CT切片的位置变异.
  • 为单片纵向分析提供了协调片的有希望的方法.
  • 在衰老研究中更准确地跟踪身体成分.