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 IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

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,...

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

Global Burden and Projections of Stroke Related to High Fasting Plasma Glucose in Young Adults: A Comparative Analysis From 1990 to 2050.

Journal of the American Heart Association·2026
Same author

Experimental Validation and Bioinformatics Analysis Elucidate the Role of MTDH-Mediated PTEN Ubiquitination and Degradation in Podocyte Injury in Diabetic Kidney Disease.

Human mutation·2026
Same author

Gradient-based rigid motion correction in CBCT via Lie algebra-constrained registration.

Physics in medicine and biology·2026
Same author

BrainUMA: A Unified multi-atlas learning framework for brain disorders diagnosis.

Medical & biological engineering & computing·2026
Same author

C[Formula: see text]Net: A co-occurrence and consistency-aware framework for structured multi-label fundus diagnosis.

Medical & biological engineering & computing·2026
Same author

Size- and Time-Dependent Impacts of Polyvinyl Chloride Microplastics on Turbot (<i>Scophthalmus maximus</i> L.): Intestinal Tolerance, Hepatic Injury, and Intestinal Microbiota Dysbiosis.

Toxics·2026

相关实验视频

Updated: May 8, 2026

3D Whole-heart Myocardial Tissue Analysis
06:53

3D Whole-heart Myocardial Tissue Analysis

Published on: April 12, 2017

8.8K

多余2D网络整合了整个心脏细分的空间相关性.

Yan Huang1, Jinzhu Yang2, Qi Sun1

  • 1Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China; School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China.

Computers in biology and medicine
|March 20, 2024
PubMed
概括

这项研究引入了一种新的深度学习网络,用于在心脏CT图像上准确地对整个心脏进行细分 (WHS). 该方法通过快速处理和低GPU内存实现了高精度,使其适合临床使用.

关键词:
这是一个2D网络.图像 图像 图像 图像 图像多机关细分化多机关细分化空间相关性 空间相关性整体心脏细分 整体心脏细分

更多相关视频

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

2.4K
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

490

相关实验视频

Last Updated: May 8, 2026

3D Whole-heart Myocardial Tissue Analysis
06:53

3D Whole-heart Myocardial Tissue Analysis

Published on: April 12, 2017

8.8K
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

2.4K
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

490

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:

  • 整心细分 (WHS) 对于心脏分析至关重要,但在准确性,速度和计算资源方面面临挑战.
  • 现有的方法往往难以满足心脏CT成像的实际临床应用的需求.

研究的目的:

  • 在心脏CT图像上开发一个准确和高效的深度学习模型来对整个心脏进行细分 (WHS).
  • 为了满足临床环境中快速推断速度和低GPU内存消耗的需求.
  • 通过先进的特征提取和注意力机制,改善心脏基层结构的划分.

主要方法:

  • 一个新的2D编码器-解码器网络,在3D心脏CT图像上集成WHS的空间相关性.
  • 使用卷积式长期短期内存跳过连接进行空间相关性特征提取.
  • 采用了具有多尺度和通道注意力的多残余解码器,用于精细的特征分析.

主要成果:

  • 实现了高细分精度,子系数为0.914和雅卡德指数为0.843.
  • 演示了9.535秒的快速推断时间和1905MB的低GPU内存消耗.
  • 在多个数据集中验证了稳定性和概括性,包括WHS和腹部器官细分挑战.

结论:

  • 拟议的多余2D网络为WHS提供了一个高度准确,高效和资源自觉的解决方案.
  • 该方法显示出在心脏成像中临床部署的巨大潜力,并适应其他器官细分任务.
  • 公共可用的源代码促进了医疗图像细分领域的进一步研究和开发.