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

5.1K
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...
5.1K

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

Explainable AI in Diagnostic Radiology for Neurological Disorders: A Systematic Review, and What Doctors Think About It.

Diagnostics (Basel, Switzerland)·2025
Same author

Edge Computing for AI-Based Brain MRI Applications: A Critical Evaluation of Real-Time Classification and Segmentation.

Sensors (Basel, Switzerland)·2024
Same author

Technologies for detecting and monitoring drivers' states: A systematic review.

Heliyon·2024
Same author

Detection of Low Resilience Using Data-Driven Effective Connectivity Measures.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2024
Same author

Cortical signals analysis to recognize intralimb mobility using modified RNN and various EEG quantities.

Heliyon·2024
Same author

SpecSeg Network for Specular Highlight Detection and Segmentation in Real-World Images.

Sensors (Basel, Switzerland)·2022
Same journal

Predicting vasovagal syncope during head-up tilt test: three machine learning approaches.

Frontiers in neuroinformatics·2026
Same journal

Decoding basal ganglia motor circuit dysfunction from handwriting: a physics-informed neural signal interpretation framework for Parkinson's disease screening.

Frontiers in neuroinformatics·2026
Same journal

FUSION-AD: interpretable AI framework for risk assessment and subgroup discovery in Alzheimer's disease.

Frontiers in neuroinformatics·2026
Same journal

A 3D-printed phantom to validate subject orientation in 3D imaging and recordings.

Frontiers in neuroinformatics·2026
Same journal

IntegriLAB: a blockchain-enabled electronic lab notebook for reproducible neuroimaging research.

Frontiers in neuroinformatics·2026
Same journal

Long-range correlations in alpha-band of electroencephalogram: a nonlinear embedding and detrended fluctuation analysis.

Frontiers in neuroinformatics·2026
查看所有相关文章

相关实验视频

Updated: Jun 27, 2025

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra
05:14

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

Published on: September 8, 2021

3.4K

大脑MRI序列和视图平面识别使用深度学习.

Syed Saad Azhar Ali1

  • 1Aerospace Engineering Department and Interdisciplinary Research Center for Smart Mobility and Logistics, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.

Frontiers in neuroinformatics
|May 8, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个深度学习模型,可以自动识别大脑MRI序列和视图平面,这对于人工智能驱动的诊断至关重要. 该系统实现了高精度,改善了用于医学成像研究的数据标签.

关键词:
这是一个辅助工具工具.大脑MRI脑部MRI脑部计算机辅助诊断是指计算机辅助的诊断.深度学习是一种深度学习.序列识别标识 序列识别视图平面 视图平面 视图平面

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
High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
10:06

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

12.9K

相关实验视频

Last Updated: Jun 27, 2025

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra
05:14

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

Published on: September 8, 2021

3.4K
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

2.4K
High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
10:06

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

12.9K

科学领域:

  • 医学成像分析 医学成像分析
  • 医疗保健中的人工智能
  • 神经科学是一个神经科学.

背景情况:

  • 大脑磁共振成像 (MRI) 数据显示了序列,视图平面和磁铁强度的显著变化.
  • 准确识别MRI参数对于自动化诊断系统和大规模数据分析中的预处理至关重要.
  • 目前用于分类MRI数据的方法可能是劳动密集型和容易出错的.

研究的目的:

  • 开发和验证深度学习 (DL) 模型,用于自动识别大脑MRI序列和视图平面.
  • 创建一个强大的分类系统,能够区分常见的MRI序列 (T1,T2加权,PD,FLAIR) 跨轴,冠状和斜面平面.
  • 为计算机辅助诊断 (CAD) 开发提供一个帮助标记大量在线数据集的工具.

主要方法:

  • 采用使用MobileNet-v2架构的深度学习方法实现了图像分类.
  • 该模型被训练在多个公开可用的脑MRI数据集上,包括各种序列和视图平面.
  • 一个12个类别的分类系统被设计来分类常见的MRI扫描类型和方向.

主要成果:

  • DL模型在未经处理的MRI扫描上达到99.76%的高精度,在没有剥离头骨的扫描上达到相似的精度.
  • 部署的工具在未见过的数据上表现出强的表现,在线来源的准确率为99.84%,医院来源的数据为86.49%.
  • 该系统有效地识别了常见的MRI序列和视图平面,验证了它的实用性.

结论:

  • 深度学习,特别是MobileNet-v2模型,为自动脑MRI序列和视图平面识别提供了有效和准确的方法.
  • 这种自动化方法显著提高了标记大型神经成像数据集的效率,支持CAD工具的进步.
  • 开发的工具为提高脑MRI数据在研究和临床环境中的质量和可用性提供了实际解决方案.