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相关概念视频

Computed Tomography01:10

Computed Tomography

6.3K
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...
6.3K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

52
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
52
Imaging Studies VII: Vascular Imaging01:19

Imaging Studies VII: Vascular Imaging

55
DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...
55
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

449
Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
449
Reducing Line Loss01:18

Reducing Line Loss

194
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
194

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相关实验视频

Updated: Sep 13, 2025

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

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多源域泛化用于学习的无损体积度生物医学图像压缩.

Dongmei Xue, Siqi Wu, Li Li

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |July 30, 2025
    PubMed
    概括

    本研究引入了一种用于无损压缩体积生物医学图像的新方法,在不同数据类型和结构中显著减少性能损失. 该方法增强了未见领域的概括性,改善了实际应用.

    科学领域:

    • 医疗成像医学成像
    • 计算机视觉 计算机视觉
    • 数据压缩数据压缩

    背景情况:

    • 学习无损压缩显著改善了比传统方法的体积生物医学图像压缩.
    • 由于模式和结构差异而产生的领域差距问题,导致未见数据的性能差.
    • 现有的方法在各种生物医学成像数据集中难以通用.

    研究的目的:

    • 为体积生物医学图像压缩开发一个多源域泛化方法.
    • 解决导致领域差距的模式和结构差异.
    • 提高学习压缩技术在未见生物医学成像领域的稳定性和适用性.

    主要方法:

    • 提出了一个自适应模式传输 (AMT) 模块,使用由嵌入在比特流中的模式特定参数控制的动态卷积.
    • 设计了一个自适应结构转移 (AST) 模块,将图像分解为LSB和MSB在波形域中的结构适应.
    • 集成的AMT和AST,使多源域通用化用于压缩.

    主要成果:

    • 尽管存在域差距,但在各种体积生物医学模式中实现了3%的性能退化.
    • 证明有效地处理模式和结构差异.
    • 展示了学习压缩方法的概括能力的显著改进.

    更多相关视频

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

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    Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
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    Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

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    Last Updated: Sep 13, 2025

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

    Published on: November 30, 2022

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

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    Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
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    Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

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    结论:

    • 拟议的多源域泛化方法有效地弥合了体积生物医学图像压缩中的域差距.
    • 这种方法可以为各种生物医学成像应用提供实用,端到端的压缩解决方案.
    • 显著减少性能损失使得学习压缩在现实世界医学成像场景中更可行.