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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

Imaging Studies III: Computed Tomography

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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: Jul 23, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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一个无监督的两步培训框架,用于低剂量计算机断层扫描.

Wonjin Kim1, Jaayeon Lee1, Jang-Hwan Choi1

  • 1Division of Mechanical and Biomedical Engineering, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, Republic of Korea.

Medical physics
|July 11, 2023
PubMed
概括

这项研究引入了一个无监督的深度学习框架,以消除低剂量的计算机断层扫描 (CT) 图像,而不需要配对数据. 该方法提高了图像质量,为医学成像提供了可复制和广泛应用的解决方案.

科学领域:

  • 医疗成像医学成像
  • 放射学中的人工智能
  • 图像删除 图像删除

背景情况:

  • 低剂量计算机断层扫描 (CT) 减少了患者的辐射暴露,但引入了图像噪声,阻碍了准确的诊断.
  • 深度神经网络,特别是卷积神经网络,显示出CT图像denoising的希望.
  • 监督学习方法需要大量的数据集对照正常和低剂量图像,这往往是不可用的.

研究的目的:

  • 提出一个新的无监督,两步培训框架,用于CT图像denoising.
  • 使用来自一个数据集的低剂量CT图像和来自另一个数据集的未配对的高剂量CT图像.
  • 为了提高低剂量CT图像的客观和感知质量.

主要方法:

  • 采用了一种两步培训流程,用于消除网络的网络.
  • 第一步涉及使用3D CT卷预测中心切片的网络预训.
  • 第二步将预先训练的网络与一个内存高效的Denoising生成对抗网络 (DenoisingGAN) 集成,以提高质量.

主要成果:

  • 与传统的机器学习和自我监督的深度学习方法相比,拟议的无监督框架显示出更高的性能.
  • 幻影和临床数据集的表现与完全监督的学习方法相比.
  • 无声化框架显著提高了噪音CT图像的客观和感知质量.
关键词:
拒绝的意思是拒绝.生成性的对抗性网络.低剂量计算机断层扫描.自己学习的自学.没有监督的学习学习.

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结论:

  • 一个新的无监督学习框架有效地消除了低剂量的CT图像,提高了客观和感知质量.
  • 该方法消除了基于物理的噪声模型或系统特定假设的需求,确保可重现性.
  • 该框架的一般适用性扩展到各种CT扫描仪和剂量水平.