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

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

Transition of the presynaptic vesicle cluster from a compact to dispersed organization during long-term potentiation.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Spatial imprints of emergent cardiomyocyte states in the pressure-overloaded heart.

bioRxiv : the preprint server for biology·2026
Same author

The COX2-PGE<sub>2</sub>-PKA Axis Suppresses Antiviral Immunity by Inhibiting mtDNA-Dependent STING Activation.

bioRxiv : the preprint server for biology·2026
Same author

VASCilia is an open-source, deep learning-based tool for 3D analysis of cochlear hair cell stereocilia bundles.

PLoS biology·2026
Same author

CURVATURE-BASED MACHINE LEARNING METHOD FOR AUTOMATED SEGMENTATION OF DENDRITIC SPINES.

bioRxiv : the preprint server for biology·2025
Same author

<i>Synaptopodin</i> KO rat for assessing the dendritic spine apparatus and axonal cisternal organelle in synaptic plasticity, development, and behavior.

bioRxiv : the preprint server for biology·2025
Same journal

Layered social competition coordinates reproductive hierarchy formation in ants.

bioRxiv : the preprint server for biology·2026
Same journal

Combination epigenetic-targeted therapy increases the immunogenicity of poorly immunogenic sarcomas.

bioRxiv : the preprint server for biology·2026
Same journal

Loss of LanC-like proteins delays post-injury regeneration of aging skeletal muscles.

bioRxiv : the preprint server for biology·2026
Same journal

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.

bioRxiv : the preprint server for biology·2026
Same journal

Confidence-supported label-free metabolic imaging with FPhaS phase autofluorescence microscopy.

bioRxiv : the preprint server for biology·2026
Same journal

Sequence-encoded autoinhibition couples mRNA decapping activity to phase separation.

bioRxiv : the preprint server for biology·2026
查看所有相关文章

相关实验视频

Updated: Jun 23, 2025

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

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

Published on: August 23, 2017

9.8K

稀疏的注释足以启动密集的细分.

Vijay Venu Thiyagarajan1, Arlo Sheridan2, Kristen M Harris1

  • 1Department of Neuroscience, Center for Learning and Memory, University of Texas at Austin, Austin Texas, 78712.

bioRxiv : the preprint server for biology
|June 25, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了一种深度学习方法,从稀疏的二维注释中创建3D大脑重建,显著减少注释时间和民主化训练数据生成,以理解神经电路.

更多相关视频

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

386
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

2.7K

相关实验视频

Last Updated: Jun 23, 2025

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

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

Published on: August 23, 2017

9.8K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

386
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

2.7K

科学领域:

  • 神经科学是一个神经科学.
  • 计算机视觉 计算机视觉
  • 生物成像是一种生物成像.

背景情况:

  • 神经回路的精确3D重建对于理解大脑功能至关重要.
  • 深度学习模型需要广泛的基础真相数据进行培训,这需要大量的劳动力来生成.
  • 在复杂的生物结构中,例如大脑神经中,注释实例细分特别具有挑战性.

研究的目的:

  • 开发一种基于深度学习的新方法,用于从稀疏的二维注释快速生成密集的3D细分.
  • 减少为生物图像分析创建培训数据所需的人力努力和时间.
  • 为了使非专家注释者能够为大规模的大脑电路映射的培训数据的生成作出贡献.

主要方法:

  • 开发了一个深度学习模型,从单个串行部分图像上的稀疏2D注释生成密集的3D细分.
  • 利用脑神经的串行断面电子显微镜数据进行方法开发和验证.
  • 训练模型使用快速生成的细分和专家注释的地面真相数据.

主要成果:

  • 这种新的方法从最小的二维注释快速生成密集的3D细分.
  • 在这些生成的细分上训练的深度学习模型的准确性与在专家注释的数据上训练的模型相提并论.
  • 标注时间减少了三倍,非专家可以生成所需的标注.

结论:

  • 开发的方法显著加速了用于生物成像中的3D实例细分的训练数据的创建.
  • 这种方法使大规模训练数据集的生成民主化,促进了大脑电路研究.
  • 这些发现为更高效,更容易获得的复杂神经结构分析铺平了道路.