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

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

7.6K
Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
7.6K
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

1.3K
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...
1.3K
Imaging Studies for Cardiovascular System IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

541
Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
541

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

Alternatively spliced killer-protector system confers S19-mediated hybrid male sterility in rice.

Nature communications·2026
Same author

RstAB activates type 1 fimbriae to promote uropathogenic <i>Escherichia coli</i> bladder invasion.

iScience·2026
Same author

NeuroLangSeg: Language-Guided Subcortical Segmentation with Pseudo-Supervision and Anatomical-Linguistic Validation.

Proceedings of machine learning research·2026
Same author

Orientation-Aware Diffusion Super-Resolution for 3T-Like Fetal MRI from Routine 1.5T Scans.

Proceedings of machine learning research·2026
Same author

Set-Membership Estimation for Switched T-S Fuzzy Systems with MDADT Switching in Tunnel Diode Circuits.

Micromachines·2026
Same author

Thymic stromal lymphopoietin promotes abdominal aortic aneurysm formation by regulating macrophage polarization.

Frontiers in immunology·2026

相关实验视频

Updated: May 3, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.4K

对比不变的自我监督细分用于定量性胎盘MRI.

Xinliu Zhong1,2, Ruiying Liu2, Emily S Nichols3,4

  • 1Department of Computer Science, Emory University, Atlanta, GA 30307, USA.

Perinatal, preterm and paediatric image analysis (2025)
|October 8, 2025
PubMed
概括

这项研究引入了T2*加权MRI中胎盘细分的新框架,尽管有回声变化,但提高了准确性. 该方法通过从多回声数据中学习强大的对比不变表示来增强定量分析.

关键词:
这就是为什么MRI是MRI.胎盘 胎盘 胎盘 胎盘分段化 分段化 分段化 分段化自主监督学习学习

更多相关视频

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

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

Published on: January 7, 2019

9.4K
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

3.7K

相关实验视频

Last Updated: May 3, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.4K
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

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

Published on: January 7, 2019

9.4K
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

3.7K

科学领域:

  • 医疗成像医学成像
  • 人工智能在医学中的应用
  • 生物医学图像分析

背景情况:

  • 精确的胎盘细分对于T2*加权MRI的定量分析至关重要.
  • 挑战包括依赖回声的对比变化和有限的手册注释.
  • 现有的方法在不同的回声时间中扎着稳定性.

研究的目的:

  • 为多回声T2*加权的胎盘MRI开发一个强大的细分框架.
  • 为了解决对比差异变化和有限的注释所带来的挑战.
  • 通过对比不变表示来改进定量分析.

主要方法:

  • 一个对比度增强的细分框架,集成面具自动编码 (MAE) 进行自我监督的预训.
  • 蒙面伪标签 (MPL) 用于跨回声时间的半监督域调整.
  • 全球-本地协作和表达一致性的语义匹配损失.

主要成果:

  • 拟议的框架证明了在回声时间之间有效的概括.
  • 在临床数据集上表现优于传统的监督细分基线.
  • 实现了强大的,对比不变的表示,用于胎盘细分.

结论:

  • 这项工作为多回声T2*加权的胎盘MRI细分提供了第一个系统框架.
  • 该方法在具有挑战性的MRI条件下提高了细分精度和稳定性.
  • 能够对胎盘结构进行更可靠的定量分析.