Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

45
Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
45
Computed Tomography01:10

Computed Tomography

4.6K
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...
4.6K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

[Retrospective analysis of 55 cases of spring thunderstorm asthma in Chongqing City].

Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine]·2025
Same author

Predicting EGFR mutation status in non-small cell lung cancer patients with brain metastases based on MRI radiomics: A systematic review and meta-analysis.

Radiography (London, England : 1995)·2025
Same author

Osteocalcin: may be a useful biomarker for early identification of rapidly progressive central precocious puberty in girls.

Journal of endocrinological investigation·2024
Same author

[Reconstruction from CT truncated data based on dual-domain transformer coupled feature learning].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University·2024
Same author

[A low- dose CT reconstruction algorithm across different scanners based on federated feature learning].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University·2024
Same author

AI's deep dive into complex pediatric inguinal hernia issues: a challenge to traditional guidelines?

Hernia : the journal of hernias and abdominal wall surgery·2023

相关实验视频

Updated: Jul 23, 2025

Contrast Enhanced Vessel Imaging using MicroCT
05:50

Contrast Enhanced Vessel Imaging using MicroCT

Published on: January 27, 2011

12.7K

[对CT图像进行半监督的基于网络的组织感知对比度增强方法]

H Zhou1,2, D Zeng1,2, Z Bian1,2

  • 1Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
|July 13, 2023
PubMed
概括

一个新的组织感知对比增强网络 (T-ACEnet) 提高了CT图像质量和器官细分精度. 这种人工智能驱动的方法增强了诊断信息,帮助医生查病变并提高了细分性能.

关键词:
图像可视化CT图像可视化计算机断层扫描 (CT) 是一种计算机断层扫描.深度学习是一种深度学习.多机关细分化的多机关细分.

更多相关视频

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
07:01

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

9.9K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.8K

相关实验视频

Last Updated: Jul 23, 2025

Contrast Enhanced Vessel Imaging using MicroCT
05:50

Contrast Enhanced Vessel Imaging using MicroCT

Published on: January 27, 2011

12.7K
3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
07:01

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

9.9K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.8K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • CT成像通常需要特定的窗口设置,以优化对不同组织的可视化.
  • 标准CT协议可能无法同时为所有器官提供全面的对比.
  • 准确的器官细分对于定量分析和治疗规划至关重要.

研究的目的:

  • 引入组织感知对比度增强网络 (T-ACEnet) 以提高CT图像对比度.
  • 评估T-ACEnet在提高CT图像器官细分精度方面的有效性.

主要方法:

  • T-ACEnet使用监督和自我监督的子网络来学习肺和软组织的最佳窗口设置.
  • 肺口罩指导监督的子网络进行组织特定的对比度调整.
  • 使用极端值抑制损失来保存器官边缘细节.

主要成果:

  • T-ACEnet生成图像与全面的窗口信息,帮助初步查损伤.
  • 与非最佳方法相比,在图像质量指标 (SSIM,QABF,VIFF,PSNR) 中观察到显著改善.
  • 使用T-ACEnet增强图像时,器官细分精度提高了4.16%,而无需改变细分模型.

结论:

  • 在CT图像中,T-ACEnet可感知增强器官组织对比度.
  • 生成的T-ACE图像提供了更丰富的诊断信息.
  • T-ACEnet显著提高了器官细分任务的性能.