Jove
Visualize
联系我们

相关概念视频

Seizures: Classification01:13

Seizures: Classification

335
Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
335
Classification of Systems-I01:26

Classification of Systems-I

179
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
179
Classification of Systems-II01:31

Classification of Systems-II

139
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
139
Classification of Connective Tissues01:30

Classification of Connective Tissues

10.5K
The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
Connective Tissue Proper
Connective tissue proper is the most abundant class of connective tissues. As its name implies, it predominantly connects different tissues in the body. Depending on the cell types, ground substance, viscosity, and fiber types in the ECM, connective tissue proper is further categorized into loose and dense....
10.5K
Classification of Signals01:30

Classification of Signals

437
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
437
Aggregates Classification01:29

Aggregates Classification

317
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
317

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 journal

Correction to "MST1 Suppression Reduces Early Brain Injury by Inhibiting the NF-κB/MMP-9 Pathway after Subarachnoid Hemorrhage in Mice".

Behavioural neurology·2026
Same journal

Social Determinants of Health and Their Association With Parkinson's Disease Prevalence in US Adults: Insights From NHANES 2001-2020.

Behavioural neurology·2026
Same journal

GLP-1 Agonist Attenuates Nicotine Reward-Related Behavior by Regulating the Prepro-Orexin in the Hypothalamus and Prepro-Glucagon in the Nucleus Tractus Solitarius.

Behavioural neurology·2026
Same journal

Using Virtual Reality to Complement Paper-and-Pencil Tests to Assess Visual Unilateral Spatial Neglect: A Feasibility Study.

Behavioural neurology·2026
Same journal

Adverse Childhood Experiences Underlie Race-Related Differences in Neural Reactivity to Stress.

Behavioural neurology·2026
Same journal

The Clinical Effect of Annonaceae Fruit Consumption on Caribbean Parkinson's Disease Severity.

Behavioural neurology·2026
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

相关实验视频

Updated: Jun 23, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

48.0K

多模式脑瘤分类使用卷积网架构.

M Padma Usha1, G Kannan1, M Ramamoorthy2

  • 1Department of Electronics and Communication Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, India.

Behavioural neurology
|June 17, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了Tumnet,这是一种深度学习方法,用于使用合并的MRI和CT图像进行脑瘤分类和细分. Tumnet实现了高精度,改善了对侵略性脑瘤的诊断和患者护理.

更多相关视频

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

42.6K
Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes
11:19

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Published on: March 20, 2018

10.4K

相关实验视频

Last Updated: Jun 23, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

48.0K
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

42.6K
Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes
11:19

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Published on: March 20, 2018

10.4K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 在瘤学瘤学.

背景情况:

  • 大脑恶性瘤是具有不良预后的侵袭性瘤,需要有效的诊断和治疗策略.
  • 像MRI,PET和CT这样的医学成像模式对于脑瘤诊断和治疗计划至关重要.
  • 精确的瘤分类和细分对于改善患者的治疗结果至关重要.

研究的目的:

  • 提出基于深度学习的多式融合成像方法用于脑瘤分类和细分.
  • 用合并的MRI和CT图像来评估拟议的Tumnet技术的性能.
  • 为了比较Tumnet在多模式和单模式 (MRI/CT) 脑瘤图像上的疗效.

主要方法:

  • 使用三种不同的方法利用308个MRI和CT脑瘤切片 (脑膜瘤和肉瘤) 的像素级融合.
  • 开发并应用了Tumnet深度学习模型,包括5个卷积,3个聚合和3个完全连接的层,具有ReLU激活.
  • 在融合多模图像和单模MRI/CT图像 (561片) 上进行脑瘤的分类和细分.

主要成果:

  • 一级统计融合指标 (平均方法) 显示SSIM组织为83%,SSIM骨为84%,准确度为90%,灵敏度为96%,特异性为95%.
  • 第二阶统计融合指标显示,融合图像的标准偏差为79%,值为0.99,表明增强功能.
  • 在融合图像上,Tumnet模型实现了高性能:96%的灵敏度,98%的准确性,99%的特异性,正常化平均值为0.75,标准偏差为0.4,偏差为0.16,为0.90.

结论:

  • 多模式融合成像与Tumnet深度学习模型相结合,显著提高了脑瘤分类和细分.
  • 提出的Tumnet技术表现出卓越的性能,为改善脑瘤诊断提供了一个有前途的工具.
  • 这些发现表明,MRI和CT图像的基于深度学习的融合可以导致更准确和可靠的脑瘤检测.