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

Effect of collagenase ointment versus silver sulfadiazine ointment on the healing of deep second-degree burns: a prospective randomised study.

Journal of wound care·2026
Same author

Prognostic significance of ypN status after neoadjuvant chemoimmunotherapy in resectable NSCLC: a systematic review and meta-analysis.

Frontiers in oncology·2026
Same author

Synergistic induction of apoptosis in lung cancer cells via TOP2A targeting through combined dihydroartemisinin and chrysin treatment.

Pulmonary pharmacology & therapeutics·2026
Same author

AI-assisted clinico-quantitative imaging nomogram for preoperative malignancy risk in solid and part-solid pulmonary nodules ≤ 3 cm: development and internal validation.

Frontiers in oncology·2026
Same author

Intrapleural hemocoagulase Bothrops atrox and early outcomes after VATS for stage IA non-small cell lung cancer.

Frontiers in medicine·2026
Same author

Retraction Note: Adipose tissue-derived stem cells suppress hypertrophic scar fibrosis via the p38/MAPK signaling pathway.

Stem cell research & therapy·2026
Same journal

AutoBiGluNet: transformer-based time series modeling for blood glucose prediction in Type 1 diabetes patients.

Health information science and systems·2026
Same journal

Multi-dimensional alignment framework with geometric intraoral constraints for precise occlusal registration.

Health information science and systems·2026
Same journal

SPSGL: uncovering psychiatric network mechanisms via structural-prior guided synaptic graph learning.

Health information science and systems·2026
Same journal

A noval 4D graph temporal brain network model for EEG-based depression detection.

Health information science and systems·2026
Same journal

PLETHSOMNet: automated identification of insomnia using deep neural network technique with photoplethysmography (PPG) signals.

Health information science and systems·2026
Same journal

Self-supervised fusion of clinical expertise and interpersonal skills for enhanced physician recommendation.

Health information science and systems·2026
查看所有相关文章

相关实验视频

Updated: Jul 8, 2025

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
12:49

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

12.8K

基于自我监督的神经网络的内镜单眼3D重建方法.

Ziming Zhang1,2, Wenjun Tan1,2, Yuhang Sun1,2

  • 1Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189 China.

Health information science and systems
|December 14, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的自我监督深度学习框架,用于单眼内镜3D重建. 该方法通过解决亮度不一致性和场景复杂性,以改善外科视觉化,在复杂的临床环境中提高准确性.

关键词:
自我运动是自我运动.内镜检查是指内镜检查.单眼深度估计的估计方法自主监督学习学习三维重建的三维重建

更多相关视频

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

2.8K
Mixed Reality Assisted Radical Endoscopic Thyroidectomy
08:06

Mixed Reality Assisted Radical Endoscopic Thyroidectomy

Published on: January 31, 2025

277

相关实验视频

Last Updated: Jul 8, 2025

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
12:49

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

12.8K
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

2.8K
Mixed Reality Assisted Radical Endoscopic Thyroidectomy
08:06

Mixed Reality Assisted Radical Endoscopic Thyroidectomy

Published on: January 31, 2025

277

科学领域:

  • 医疗成像医学成像
  • 计算机视觉 计算机视觉
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 使用深度学习的单眼视觉3D重建已在传统领域建立.
  • 自主监督的深度学习对于医疗内镜成像至关重要,因为数据采集的挑战.
  • 现有的内镜3D重建研究主要是基于实验室的,缺乏临床环境适应性.

研究的目的:

  • 为复杂的临床外科环境开发一个强大的单眼内镜3D重建框架.
  • 为了提高从内镜视频数据3D重建的准确性和可靠性.
  • 为了应对临床内镜成像中不一致的亮度和复杂的场景细节等挑战.

主要方法:

  • 使用基于光流的神经网络来管理对的亮度变化.
  • 集成了注意力模块,以加强对像素纹理和深度差异的关注.
  • 层间损失被用于重建网络内的多层监督.

主要成果:

  • 建立了一个完整的单眼内镜3D重建框架,并在临床数据集上进行验证.
  • 与其他自我监督方法相比,拟议的框架在内镜运动期间展示了框架映射的优异模拟.
  • 使用交叉相关系数指标的定量实验证实了该框架的有效性.

结论:

  • 开发的框架成功地解决了用于3D重建的临床内镜场景的复杂性.
  • 该方法显示出优秀的概括能力,在外部数据集上表现良好,如SCARED.
  • 这项工作为实际的临床应用推进了自我监督的内镜3D重建.