Jove
Visualize
联系我们

相关概念视频

Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

286
Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
286

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

Absorbable Micro-Aesthetic Lifting Thread for Nasolabial Fold Correction: A Multicenter, Randomized, Evaluator-Blinded, Parallel-Controlled Clinical Trial.

Aesthetic plastic surgery·2026
Same author

Evaluation of mid-treatment <sup>18</sup>F-FDG PET changes in the oral cavity as a predictor of radiation-induced mucositis severity.

Quantitative imaging in medicine and surgery·2026
Same author

Quantitative mid-treatment imaging biomarkers for response prediction after radiotherapy in head and neck cancer: a systematic review and meta-analysis.

EJNMMI research·2026
Same author

OCT-based optic neuropathy diagnosis using explainable and privacy-preserving machine learning.

Scientific reports·2026
Same author

Progress of Plastic and Aesthetic Industry in Mainland China: A National Data Comparison between Public and Private Hospitals.

Aesthetic plastic surgery·2026
Same author

Physicochemical Reinforcement Unlocks Sterilization-Stable Anisotropic Hydrogels for Cell-Compatible Mock Arteries.

Advanced healthcare materials·2026
Same journal

Kolmogorov-Arnold Guided Local-Global Attention for Medical Image Classification.

Journal of imaging informatics in medicine·2026
Same journal

Artificial Intelligence-Assisted Inner Ear Computed Tomography Analysis: Radiomics-Based Comparison of Affected and Unaffected Ears in Idiopathic Sudden Sensorineural Hearing Loss.

Journal of imaging informatics in medicine·2026
Same journal

High Adoption, Higher Expectations: A Cross-Sectional Survey of Radiologist Engagement with Artificial Intelligence in the United Arab Emirates.

Journal of imaging informatics in medicine·2026
Same journal

Complex-valued Multi-scale Hybrid Attention Network for Fast MRI via Sparsified Data Learning.

Journal of imaging informatics in medicine·2026
Same journal

Automatic Phase and Sequence Identification in Gd-EOB-DTPA-Enhanced Liver MRI Using Deep Convolutional and Sequential Learning.

Journal of imaging informatics in medicine·2026
Same journal

Ultrasound-Based AI in Predicting Hormone Receptor Status in Breast Cancer: Is "Digital Biopsy" Possible.

Journal of imaging informatics in medicine·2026
查看所有相关文章
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: Jan 17, 2026

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

3.3K

为CT冠状动脉细分优化可通用深度学习模型:多因素评估

Shisheng Zhang1, Ramtin Gharleghi2, Sonit Singh3

  • 1School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, Australia. shisheng.zhang@unsw.edu.au.

Journal of imaging informatics in medicine
|September 19, 2025
PubMed
概括
此摘要是机器生成的。

冠状动脉细分的深度学习模型显示了更好的图像对比度和清晰度的提高准确性,但化会对性能产生负面影响. 研究结果强调,需要考虑成像特征和血管解剖学,以进行强大的CAD管理.

关键词:
在CTCA中,CTCA是CTCA.计算机断层扫描冠状动脉血管学卷积神经网络是一种卷积神经网络.冠状动脉细分的划分深度学习是一种深度学习.模型的概括性模型的概括性.

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

43.4K

相关实验视频

Last Updated: Jan 17, 2026

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

3.3K
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

43.4K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 心血管疾病 心血管疾病

背景情况:

  • 冠状动脉疾病 (CAD) 是全球主要的健康问题,推动了对先进诊断工具的需求.
  • 自动化医疗图像细分,特别是使用深度学习,为改进CAD管理和诊断提供了潜力.
  • 目前的深度学习模型面临的挑战是,由于成像和患者因素的变化,在各种数据集中实现一致的性能.

研究的目的:

  • 通过深度学习研究图像质量和分辨率对冠状动脉细分精度的影响.
  • 评估诸如容器大小,化,对比度增强和边缘度等因素如何影响细分性能.
  • 提供数据驱动的基础,为冠状动脉细分开发更可概括的深度学习模型.

主要方法:

  • 利用两个数据集 (ASOCA和GeoCAD) 来训练和验证深度学习模型.
  • 实施并比较了三个深度学习架构:U-Net,Swin-UNETR和EfficientNet-LinkNet.
  • 评估了成像特征 (对比度与噪声比,动脉对比度增强,边缘清晰度) 和化程度对细分精度的影响.

主要成果:

  • 动脉对比度增强 (r=0.408,p<0.001) 和边缘度 (r=0.239,p=0.046) 与改善的细分显著相关.
  • 化对所有严重程度的细分精度产生了负面影响,低化构成了最重要的挑战 (p<0.05).
  • 较大的血管直径 (男性的OM1,女性的LM和RCA) 与这些特定血管的更好的细分性能有关.

结论:

  • 像对比度增强和边缘度这样的图像质量指标对于准确的冠状动脉细分至关重要.
  • 化对细分精度构成重大障碍,需要有针对性的算法改进.
  • 考虑到解剖学变异性,例如血管直径,对于提高CAD分析中深度学习模型的概括性至关重要.