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

Random Sampling Method01:09

Random Sampling Method

12.3K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
12.3K
Random Variables01:09

Random Variables

13.4K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
13.4K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

140
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
140
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

124
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
124

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

Linking maternal blood pressure with fetal cerebral haemodynamics and cortical growth in congenital heart disease.

EBioMedicine·2026
Same author

Equivariant Filters for Efficient Tracking in 3D Imaging.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same author

Learning-based non-linear registration robust to MRI-sequence contrast.

Proceedings of the International Society for Magnetic Resonance in Medicine ... Scientific Meeting and Exhibition. International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition·2026
Same author

Improved, rapid fetal-brain localization and orientation detection for auto-slice prescription.

Proceedings of the International Society for Magnetic Resonance in Medicine ... Scientific Meeting and Exhibition. International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition·2026
Same author

Fast, automated slice prescription of standard anatomical planes for fetal brain MRI.

Proceedings of the International Society for Magnetic Resonance in Medicine ... Scientific Meeting and Exhibition. International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition·2026
Same author

Evaluating reliability of automated quantitative brain morphometry from fetal T2-weighted MRI.

Frontiers in neuroscience·2026
Same journal

ChartQA-X: Generating Explanations for Visual Chart Reasoning.

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision·2026
Same journal

PALMS+: Modular Image-Based Floor Plan Localization Leveraging Depth Foundation Model.

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision·2026
Same journal

Generative Model-Based Fusion for Improved Few-Shot Semantic Segmentation of Infrared Images.

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision·2026
Same journal

ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders.

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision·2025
Same journal

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network.

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision·2025
Same journal

MGM-AE: Self-Supervised Learning on 3D Shape Using Mesh Graph Masked Autoencoders.

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision·2025
查看所有相关文章

相关实验视频

Updated: Sep 9, 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

2.9K

AnyStar:域随机的通用恒星形3D实例分割

Neel Dey1, S Mazdak Abulnaga1, Benjamin Billot1

  • 1MIT CSAIL.

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision
|August 29, 2025
PubMed
概括
此摘要是机器生成的。

AnyStar生成合成数据用于训练恒星凸实例细分网络. 这种方法消除了对数据集特定的注释的需求,使各种生物成像方式的通用细分成为可能.

更多相关视频

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.7K
Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
06:18

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

1.2K

相关实验视频

Last Updated: Sep 9, 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

2.9K
From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.7K
Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
06:18

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

1.2K

科学领域:

  • 生物医学图像分析
  • 计算机视觉
  • 医学成像

背景情况:

  • 在生物显微镜和放射学中,星形状如核和结节是常见的.
  • 目前的实例细分方法需要广泛的,特定于数据集的注释,这阻碍了广泛的应用.
  • 由于成像属性的变化,调整模型以适应新的数据集或成像模式需要进行重大重新设计.

研究的目的:

  • 开发用于恒星凸形状的通用实例细分网络.
  • 克服手动注释和特定领域模型的局限性.
  • 创建适用于各种生物和医学成像数据集的强大方法.

主要方法:

  • 介绍了AnyStar,一个域随机生成模型来合成现实的训练数据.
  • 模拟的斑块状物体随机的外观,环境和成像物理.
  • 在生成的合成数据上训练一个单一实例细分网络.

主要成果:

  • 用AnyStar训练的网络可以将未见的数据集概括,而无需重新训练或微调.
  • 实现核 (C. elegans,P. dumerilii,小鼠皮质,斑马鱼大脑) 和胎盘细胞核 (人类胎儿MRI) 的精确3D细分.
  • 在光显微镜,微型CT,EM和MRI模式中表现出强大的性能.

结论:

  • 通过AnyStar的合成数据方法,可以开发多功能实例细分网络.
  • 这种方法大大减少了手动注释和领域调整的需要.
  • 这种方法有望在生物显微镜和放射学中推进自动化分析.