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

Computed Tomography01:10

Computed Tomography

4.5K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
4.5K

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

Extracellular Space Barrier Dysfunction Disrupts Interstitial Fluid Drainage and Is Associated with Memory Heterogeneity in Cognitive Aging.

Aging and disease·2026
Same author

Divergent scalp-to-region distance alteration patterns in autism spectrum disorders, Parkinson's disease and Alzheimer's disease.

bioRxiv : the preprint server for biology·2026
Same author

Corrigendum to "Preventive effects of a standardized flavonoid extract of safflower in rotenone-induced Parkinson's disease rat model" [Neuropharmacology 217 (2022) 109209].

Neuropharmacology·2026
Same author

Neural Representation of Exogenous and Endogenous Temporal Expectations Based on fMRI.

CNS neuroscience & therapeutics·2026
Same author

Towards a general-purpose foundation model for functional MRI analysis.

Nature biomedical engineering·2026
Same author

Brain Extracellular Space: From an Overlooked Dimension to Catalyst of a Novel Neuroscience Paradigm.

Cyborg and bionic systems (Washington, D.C.)·2026
Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026
Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026
Same journal

The Ritz Adjoint Method for MRI Pulse Design.

IEEE transactions on medical imaging·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026
查看所有相关文章

相关实验视频

Updated: Jun 27, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K

PCNet:用于CT通用细分模型的先行类别网络.

Yixin Chen, Yajuan Gao, Lei Zhu

    IEEE transactions on medical imaging
    |April 30, 2024
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了优先类别网络 (PCNet),用于改进计算机断层扫描 (CT) 图像分割. PCNet利用先前的解剖学知识来提高医疗图像细分的准确性和稳定性.

    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
    Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
    05:56

    Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

    Published on: April 14, 2023

    2.5K

    相关实验视频

    Last Updated: Jun 27, 2025

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.7K
    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
    Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
    05:56

    Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

    Published on: April 14, 2023

    2.5K

    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:

    • 计算机断层扫描 (CT) 图像中解剖结构的准确细分对于临床应用至关重要.
    • 目前的深度学习细分方法由于数据规模和模型大小而面临限制.
    • 临床诊断,治疗计划和疾病监测在很大程度上依赖于精确的CT图像细分.

    研究的目的:

    • 开发一种新的深度学习方法,即优先类别网络 (PCNet),以改进CT图像细分.
    • 利用解剖学类别之间的先前知识来提高细分性能.
    • 创建一个高性能,通用型的CT细分模型,以提高精度和稳定性.

    主要方法:

    • 拟议的PCNet框架有三个组成部分:先前类别提示 (PCP),层次类别系统 (HCS) 和层次类别丢失 (HCL).
    • PCP使用对比语言图像训练 (CLIP) 和注意力模块来定义解剖类别之间的关系.
    • HCS通过等级关系指导细分,而HCL通过一致性和定向指导来强制执行.

    主要成果:

    • 在广泛的实验中,PCNet证明了CT细分的高性能和通用适用性.
    • 该框架在多个下游任务上显示出显著的可转移性.
    • 废弃实验证实了HCS方法的关键重要性.

    结论:

    • 通过结合先前的解剖学知识,PCNet有效地增强了CT图像细分.
    • 拟议的方法为医疗图像分析提供了强大且可转移的解决方案.
    • PCNet框架代表了医疗成像细分深度学习的重大进步.