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

6.1K
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...
6.1K
Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

5.3K
Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
5.3K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

50
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...
50
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

2.5K
Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
2.5K
Positron Emission Tomography01:29

Positron Emission Tomography

5.4K
Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
5.4K
Confocal Fluorescence Microscopy01:16

Confocal Fluorescence Microscopy

14.2K
Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
14.2K

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

Macrophage-secreted brain-derived neurotrophic factor promotes tumor growth in triple-negative breast cancer by inducing axonogenesis.

Cell death and differentiation·2026
Same author

Human donor liver viability evaluation with polarization-sensitive optical coherence tomography.

Science translational medicine·2026
Same author

Advancing Organ Preservation and Perfusion: Introducing the International Society of Organ Preservation and Perfusion Therapy (ISOPPT).

Artificial organs·2026
Same author

Multi-contrast optical coherence tomography for <i>in vivo</i> visualization and quantification of vascular features and collagen in mouse ovaries in aging.

Biomedical optics express·2026
Same author

Electrically enhanced, Nature-Driven microbial attenuation of chromate and dichloromethane in groundwater.

Bioresource technology·2026
Same author

Exploring role of therapeutic plasma exchange for hepatitis A-related acute liver failure: An Indian multi-center cohort study.

Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology·2026
Same journal

A Multi-Head Attention Transformer Model for Wearable in Situ Fall Detection.

IEEE access : practical innovations, open solutions·2026
Same journal

Validating Single-Camera Pose Estimation Against Multi-Camera Motion Capture for Accessible Biomechanical Assessment.

IEEE access : practical innovations, open solutions·2026
Same journal

Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification.

IEEE access : practical innovations, open solutions·2026
Same journal

Radio-Frequency Toroid Susceptometry of Magnetic Nanoparticles: What Goes Around Comes Around.

IEEE access : practical innovations, open solutions·2026
Same journal

Cross-Architecture Knowledge Distillation for Histopathological Image Analysis.

IEEE access : practical innovations, open solutions·2026
Same journal

Mislabel Identification Using Transfer Learning-Based Ensemble Method.

IEEE access : practical innovations, open solutions·2026
查看所有相关文章

相关实验视频

Updated: Sep 9, 2025

In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography
07:44

In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography

Published on: July 24, 2020

3.0K

一个轻量级的预训练模型用于光学一致性断层扫描

Haoyang Cui1, Chen Wang2, Paul Calle1

  • 1School of Computer Science, Gallogly College of Engineering, The University of Oklahoma, Norman, OK 73019, USA.

IEEE access : practical innovations, open solutions
|August 28, 2025
PubMed
概括
此摘要是机器生成的。

新的深度学习模型Octascope增强了光学一致性断层扫描 (OCT) 图像分析. 通过使用多领域预训练,实现实时临床应用的高精度和更快的速度.

关键词:
深度学习医学成像技术八度镜特定于一个领域基础模型轻量级的转移学习

更多相关视频

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography
11:21

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Published on: January 15, 2013

11.6K
Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
12:22

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

Published on: August 4, 2018

8.6K

相关实验视频

Last Updated: Sep 9, 2025

In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography
07:44

In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography

Published on: July 24, 2020

3.0K
Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography
11:21

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Published on: January 15, 2013

11.6K
Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
12:22

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

Published on: August 4, 2018

8.6K

科学领域:

  • 生物医学成像
  • 医疗人工智能
  • 医疗图像分析的深度学习

背景情况:

  • 光学连贯断层扫描 (OCT) 提供高分辨率的地下组织成像.
  • 对于海外国家和地区的分析,深度学习面临着有限的培训数据和缓慢的推断速度的挑战.
  • 开发有效和准确的OCT人工智能模型对于临床应用至关重要.

研究的目的:

  • 开发一个轻量级的,特定于域的卷积神经网络 (CNN) 模型,用于有效和准确的OCT图像分析.
  • 提高跨不同组织类型的OCT分析模型的通用性.
  • 在实时的OCT应用中实现计算效率和诊断精度之间的平衡.

主要方法:

  • 开发了一种用于OCT图像分析的轻量级CNN模型Octascope.
  • 采用课程学习方法进行预培训:自然图像 (ImageNet) 接下来是多种OCT组织 (视网膜,腹部,脏).
  • 评估了Octascope的外周组织检测和视网膜诊断任务,并将其与现有方法和基于变压器的模型进行比较.

主要成果:

  • 通过Octascope检测外周组织的准确性提高了 (比单任务学习提高了9. 13%,比特定于OCT的转移学习提高了5. 95%).
  • 在视网膜诊断方面,Octascope的表现优于VGG16 (5. 36%) 和ResNet50 (6. 66%).
  • 与RETFound相比,Octascope的推断速度快2至4. 4倍,准确度相似或更高.

结论:

  • 在OCT图像分析方面,Octascope提供了显著的进步,平衡了计算效率和诊断准确性.
  • 多领域预训练策略提高了模型在不同组织类型中的通用性.
  • 它适用于需要快速可靠的OCT图像解释的实时临床应用.