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相关概念视频

Computed Tomography01:10

Computed Tomography

4.2K
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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相关实验视频

Updated: Jun 1, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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深度学习模型用于CT图像分类:综合文献综述

Isah Salim Ahmad1,2, Jingjing Dai1,2, Yaoqin Xie1,2

  • 1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

Quantitative imaging in medicine and surgery
|January 22, 2025
PubMed
概括

深度学习 (DL) 显著增强了计算机断层扫描 (CT) 图像分析,用于检测COVID-19和肺结节等疾病. 先进的DL模型提高了诊断的准确性和效率,尽管在实施方面仍然存在挑战.

关键词:
计算机断层扫描 (CT) 扫描冠状病毒疾病2019 (COVID-19) 是一种新型冠状病毒疾病.深度学习 (DL) 是指深度学习.基础模型 基础模型结节检测 结节检测

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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相关实验视频

Last Updated: Jun 1, 2025

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科学领域:

  • 医学成像和诊断 医学成像和诊断
  • 医疗保健中的人工智能
  • 放射学和瘤学 放射学和瘤学

背景情况:

  • 计算机断层扫描 (CT) 对于诊断严重疾病,特别是呼吸系统疾病和癌症至关重要.
  • 深度学习 (DL) 正在彻底改变CT图像分析,提高诊断准确性和效率.
  • 本综述侧重于DL在COVID-19检测和肺结节分类中的应用,使用CT.

研究的目的:

  • 审查先进的深度学习方法对CT成像分析的影响.
  • 突出DL在COVID-19检测和肺结节分类中的应用.
  • 探索DL架构在医学成像中的演变.

主要方法:

  • 从2013年到2023年对CT图像分析中的DL进行了全面的文献搜索.
  • 研究了从卷积神经网络 (CNNs) 到基础模型 (FMs) 的演变.
  • 专注于同行评审的研究和来自主要数据库的评论文章.

主要成果:

  • 深度学习,特别是基础模型,已经改变了CT图像分析,提高了诊断能力.
  • 在COVID-19检测和肺癌查方面观察到重大进展.
  • 技术挑战包括数据变化,数据集大小和计算需求,其中包括转移学习和数据增强等解决方案.

结论:

  • 深度学习在推进COVID-19的CT分析和肺结节检测方面发挥着关键作用.
  • 将DL模型集成到临床工作流程中,有望提高诊断准确性和效率.
  • 持续的研究,合作和道德考虑对于DL的临床整合至关重要.