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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 I: CT and MRI01:14

Imaging Studies I: CT and MRI

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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
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X-ray Imaging01:24

X-ray Imaging

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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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Radiological Investigation I: X-ray and CT01:30

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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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从计算机断层扫描图像中检测COVID-19使用切片处理技术和修改的Xception分类器.

Kenan Morani1, Esra Kaya Ayana2, Dimitrios Kollias3

  • 1Izmir Democracy University Uckuyular, Gursel Aksel Blv No: 14 35140, Karabaglar, Izmir, Türkiye.

International journal of biomedical imaging
|June 17, 2024
PubMed
概括

这项研究通过使用精益转移学习模型和改进的图像处理来增强CT扫描中的COVID-19检测. 该方法在诊断2019年新冠肺炎疾病时显示出更高的准确性.

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3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
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Image Rendering Techniques in Postmortem Computed Tomography: Evaluation of Biological Health and Profile in Stranded Cetaceans
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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
  • 计算机科学 计算机科学

背景情况:

  • 准确的COVID-19诊断对于患者管理和公共卫生至关重要.
  • 计算机断层扫描 (CT) 图像是检测COVID-19的一个关键工具.
  • 现有的诊断模型需要改进,以提高准确性和效率.

研究的目的:

  • 开发一种使用CT图像进行COVID-19检测的增强计算方法.
  • 通过减少模型错误分类来提高COVID-19诊断的准确性.
  • 利用转移学习来高效有效地分析医学成像数据.

主要方法:

  • 使用基于Xception架构的精益转移学习模型.
  • 图像预处理涉及去除极端切片和手动的肺部区域裁剪.
  • CT扫描被调整为224x224的尺寸,并使用修改的Xception模型进行处理.
  • 该模型在一个大型,严格注释的CT图像数据库 (>5000名患者) 上进行了训练和验证.

主要成果:

  • 与以前的解决方案和基线模型相比,增强方法显示了更好的性能.
  • 拟议的方法在COV19-CT数据库上实现了与表现最好的方法相匹配的性能.
  • 在未见的图像上的验证证实了模型在测试分区上的有效性.

结论:

  • 开发的转移学习模型为通过CT扫描准确检测COVID-19提供了一个有希望的方法.
  • 图像处理技术有效地减少了错误分类,并突出了相关的肺部特征.
  • 未来的研究应该侧重于可扩展性,适应性和与先进的图像分析技术的集成.