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相关概念视频

Brain Imaging01:14

Brain Imaging

225
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
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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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Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

2.4K
Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
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相关实验视频

Updated: Jun 24, 2025

Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

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结构细分的无监督模型应用于大脑计算机断层扫描.

Paulo Victor Dos Santos1,2,3, Marcella Scoczynski Ribeiro Martins1,4, Solange Amorim Nogueira1,2

  • 1Electrical, Mechanical & Computer Engineering School, Federal University of Goias, Goiania, Brazil.

PloS one
|June 13, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种无监督的方法来细分脑部计算机断层扫描 (CT) 扫描. 这种方法简化了异常识别,减少了医疗专家的成本和时间.

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
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相关实验视频

Last Updated: Jun 24, 2025

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Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
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科学领域:

  • 医学成像分析分析 医学成像分析
  • 计算解剖学的计算解剖学
  • 医疗保健中的人工智能

背景情况:

  • 脑电脑断层扫描 (CT) 扫描的准确细分对于诊断神经疾病至关重要.
  • 现有的细分方法往往需要大量的训练数据或手动干预,这限制了它们的临床适用性.
  • 对于现实世界的医疗数据集,需要有效和自动化的细分技术.

研究的目的:

  • 开发一种无监督的方法,在脑CT扫描中对解剖头部结构进行细分.
  • 提供一个工具,帮助医疗专家识别异常区域,以改善诊断.
  • 为了减少与图像细分相关的计算工作,培训时间和财务成本.

主要方法:

  • 该方法采用无监督的图像特征提取.
  • 类似性和连续性约束用于生成细分图.
  • 使用空间连续性评分函数,根据所需的解剖结构数量进行量身定制.

主要成果:

  • 拟议的方法成功地在脑CT扫描中对解剖头部结构进行细分.
  • 该方法是为现实世界数据集设计的,提供了一个简化和可访问的解决方案.
  • 该方法显示了减少计算资源和培训需求的潜力.

结论:

  • 这种无监督细分方法为分析大脑CT扫描提供了一个实用的工具.
  • 该技术可以加快对异常扫描的解释,可能会影响临床实践.
  • 该方法有助于在研究和临床环境中推进医学图像分析.