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

Deformation of Member under Multiple Loadings01:11

Deformation of Member under Multiple Loadings

184
When a rod is made of different materials or has various cross-sections, it must be divided into parts that meet the necessary conditions for determining the deformation. These parts are each characterized by their internal force, cross-sectional area, length, and modulus of elasticity. These parameters are then used to compute the deformation of the entire rod.
In the case of a member with a variable cross-section, the strain is not constant but depends on the position. The deformation of an...
184
Deconvolution01:20

Deconvolution

186
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
186
Structural Classification of Joints01:20

Structural Classification of Joints

3.5K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.5K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

127
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
127
Functional Classification of Joints01:09

Functional Classification of Joints

4.2K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
4.2K
Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

4.8K
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...
4.8K

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

Laser-programmable glycosaminoglycan-based nanocarriers co-deliver hypocrellin B and doxorubicin for spatiotemporal chemo-photothermal therapy of hepatocellular carcinoma.

Colloids and surfaces. B, Biointerfaces·2026
Same author

CT-based radiogenomic prediction of ICAM1 and RAET1E as biomarkers of NK cytotoxicity in clear cell renal cell carcinoma.

Frontiers in immunology·2026
Same author

Mesoporous polydopamine nanoplatform enhances IL-2 immunotherapy for hepatocellular carcinoma via mild photothermal therapy and lactate regulation.

Asian journal of pharmaceutical sciences·2026
Same author

Reversing immunotherapy resistance in cold tumors by weaponizing pyroptosis with a dual-payload nanotuner.

Journal of controlled release : official journal of the Controlled Release Society·2026
Same author

Supercharged ferritin nanocages enable universal cytosolic protein delivery.

Nature communications·2026
Same author

Towards advancing cancer nanomedicine: Recent strategies for overcoming translational and toxicological challenges.

Biomaterials advances·2026
Same journal

Non-invasive classification of stable HFpEF using a deep learning model trained on acoustic features of sustained vowels.

Biomedical engineering online·2026
Same journal

Lung cancer multimodal auxiliary diagnosis based on entropy weight decision fusion.

Biomedical engineering online·2026
Same journal

Potentials of BMSCs for regulating osteogenic-vascular-neural-lymphatic coupling in bone regeneration.

Biomedical engineering online·2026
Same journal

Protein adsorption at material interface: mechanistic design framework for engineering ceramic scaffolds for bone repair applications.

Biomedical engineering online·2026
Same journal

Machine learning models of segmentation in acute ischemic stroke: a systematic review and meta-analysis.

Biomedical engineering online·2026
Same journal

The influence of successful septal myectomy on myocardial stress distributions in the left ventricle: a computational analysis.

Biomedical engineering online·2026
查看所有相关文章

相关实验视频

Updated: Jul 16, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.2K

macJNet:使用联合学习框架和多样化级联MIND的弱监督多式模式图像可变形注册.

Zhiyong Zhou1,2, Ben Hong3, Xusheng Qian1,2

  • 1Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China.

Biomedical engineering online
|September 19, 2023
PubMed
概括
此摘要是机器生成的。

macJNet使用一种新的联合学习框架和模式独立描述器准确地对齐多模式医疗图像. 这种弱监督的方法增强了跨模态特征表示,以改善医疗图像注册.

关键词:
可变形的注册表可以变形.图像描述器 图像描述器联合学习 (joint learning) 是一种联合学习方式.多式联络是多式联络.半监督的细分是半监督的细分.

更多相关视频

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

8.0K
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

626

相关实验视频

Last Updated: Jul 16, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.2K
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

8.0K
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

626

科学领域:

  • 医学图像分析 医学图像分析
  • 计算机视觉 计算机视觉
  • 放射学 放射学是一门学科.

背景情况:

  • 可变形的多模式图像注册对于医学图像分析至关重要,但由于强度扭曲和大变形而具有挑战性.
  • 很难确定不同模式 (如CT和MRI) 的图像之间的精确密度对应.
  • 现有的方法与显著的交叉模式变化和复杂的解剖结构作斗争.

研究的目的:

  • 引入macJNet,一种低监督的方法,用于准确的可变形多式联络医疗图像记录.
  • 开发一个联合的学习框架,整合注册和细分网络.
  • 为增强特征表示提出一种新的模式独立邻里描述符 (macMIND).

主要方法:

  • macJNet采用一个联合学习框架,包括一个注册网络和两个半监督细分网络.
  • 一个多采样级联模式的独立社区描述符 (macMIND) 捕捉了跨方向和尺度的自我相似性上下文.
  • 该框架利用细分网络进行语义对应,并通过注册一致性改善细分.

主要成果:

  • 与最先进的方法相比,macJNet在多式联络医疗图像注册方面表现出卓越的性能.
  • 拟议的macMIND描述符有效地增强了用于注册的跨模式特征表示.
  • 联合学习框架提高了注册准确性和细分性能.

结论:

  • macJNet为可变形的多式联络医疗图像注册提供了强大而有效的解决方案.
  • 麦克明德描述符和联合学习框架显著推进了跨模式医疗图像分析领域.
  • 这种方法有望改善临床环境中的诊断准确性和治疗规划.