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

Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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

Artificial Intelligence in Spine Neuroimaging: Diagnostic and Prognostic Utility of Novel Biomarkers in Lower Back Pain.

Journal of clinical medicine·2026
Same author

Nanomedicine in Cardiovascular Inflammation: Novel Diagnostic and Therapeutic Strategies.

Journal of personalized medicine·2026
Same author

Planimetric and Linear MRI Markers for Progressive Supranuclear Palsy Classification: A Large Multicohort International Study.

Radiology·2026
Same author

Extreme Variability of the Kidney Hilar Architecture: A Radioanatomical Map to Guide Surgical Approaches.

Diagnostics (Basel, Switzerland)·2026
Same author

Oculomotor Nerve Palsy-Etiologies, Symptoms and Diagnosis: A Systematic Review with Meta-Analysis.

Diagnostics (Basel, Switzerland)·2026
Same author

A Rare Variation of Coeliac Trunk Hexafurcation.

Diagnostics (Basel, Switzerland)·2026
Same journal

Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.

Journal of imaging·2026
Same journal

Coronary Artery Anomalies and Anatomical Variants: Cross-Sectional Diagnostic Imaging and Clinical Background.

Journal of imaging·2026
Same journal

YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.

Journal of imaging·2026
Same journal

Radiomics-Guided Multi-Sequence Learning for Pathological Complete Response Prediction from Breast MRI with Missing Auxiliary Sequences.

Journal of imaging·2026
Same journal

Cutaneous Thermography in Arthropathies: Quantitative Imaging, Machine Learning, and Clinical Translation.

Journal of imaging·2026
Same journal

Two-Stage Dynamic Synergistic Segmentation Method for Myocardial Pathology.

Journal of imaging·2026
查看所有相关文章

相关实验视频

Updated: Jun 7, 2026

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

39.8K

探索多病理大脑细分:从基于体积的到基于组件的深度学习分析.

Ioannis Stathopoulos1,2, Roman Stoklasa2,3, Maria Anthi Kouri1

  • 12nd Department of Radiology, Medical School, Attikon University Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece.

Journal of imaging
|January 24, 2025
PubMed
概括
此摘要是机器生成的。

人工智能模型在MRI扫描上对细分大脑异常有希望. 需要进一步分析以了解模型性能,以了解异常的位置,强度和临床应用的体积.

关键词:
人工智能算法的人工智能算法深度学习是一种深度学习.磁共振成像 (MRI) 的使用.多发性硬化症 (MS) 是一种疾病.细分化 细分化的细分化脑中风,中风,中风.这些都是瘤,瘤.白质超强度 (WMH) 是指白质的超强度.

更多相关视频

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.1K
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

2.2K

相关实验视频

Last Updated: Jun 7, 2026

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

39.8K
Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.1K
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

2.2K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 神经科学是一个神经科学.

背景情况:

  • 人工智能算法越来越多地用于在MRI扫描中检测和细分大脑异常.
  • 虽然人工智能模型实现了高精度,但对其失败的深入评估对于临床整合至关重要.
  • 评估的关键因素包括异常的位置,强度和体积.

研究的目的:

  • 分析预先训练的U-net模型在四种病理的脑MRI扫描上进行细分性能.
  • 为了评估模型在分割整个异常体积和单个异常组件的准确性.
  • 调查细分错误 (真正,假负,假正) 和异常特征之间的关系.

主要方法:

  • 使用预先训练的U-net模型对一组脑MRI扫描进行验证.
  • 使用子得分系数 (DSC),灵敏度和精度评估整个异常体积的细分性能.
  • 分析了个别异常组件的检测和细分,将结果分类为正确,部分,错过或假阳性.

主要成果:

  • 对于整个异常体积,该模型实现了0.76的DSC,0.78的灵敏度和0.82.8的精度.
  • 对于单个异常组件,48.8%是正确细分的 (DSC≥0.5),27.1%是部分细分的 (0.05>DSC>0.5),24.1%是错过的 (错误负).
  • 该模型产生了25.1%的假阳性,进一步分析将错误与异常位置,强度 (FLAIR,T2,T1ce) 和体积相关联.

结论:

  • U-net模型在细分大脑异常方面表现出显著的能力,但需要进一步细化以获得精确的临床应用.
  • 了解与异常特征相关的故障模式对于提高神经成像中AI模型可靠性至关重要.
  • 对细分错误的详细分析为开发更强大的放射实践AI工具提供了洞察力.