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

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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...

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

Energy-Based Phase-Locking State Analysis in Brain State Identification.

Human brain mapping·2026
Same author

Neuromodulation-induced normalization of cortical metastable dynamics signatures in Parkinson's disease.

NPJ Parkinson's disease·2026
Same author

From relay station to circuit hub: Thalamic subnuclear precision and the major depressive disorder dysfunctome.

Psychiatry and clinical neurosciences·2026
Same author

CShaperApp: Segmenting and analyzing cellular morphologies of the developing <i>Caenorhabditis elegans</i> embryo.

Quantitative biology (Beijing, China)·2026
Same author

An effective method for quantification, visualization, and analysis of 3D cell shape during early embryogenesis.

Quantitative biology (Beijing, China)·2026
Same author

Learning Optimal Spectral Clustering for Functional Brain Network Generation and Classification.

IEEE journal of biomedical and health informatics·2026

相关实验视频

Updated: Jul 14, 2026

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

2.2K

使用YOLO和生成对抗网络进行最小侵入性机器人手术的手术仪器部件的阻塞弹性实例细分.

Houssameddine Hamdi, Chenfei Ye, Sulayman Ahmad

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    概括

    SurgSeg-GAN通过精确分段仪器来提高机器人手术的安全性,即使有遮. 这种混合框架提高了微创手术的精度.

    更多相关视频

    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

    1.5K
    Robotics in Surgery: A Modular Robotic Platform Driven Gastric Wedge Resection
    07:27

    Robotics in Surgery: A Modular Robotic Platform Driven Gastric Wedge Resection

    Published on: February 7, 2025

    950

    相关实验视频

    Last Updated: Jul 14, 2026

    Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
    09:10

    Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

    Published on: August 5, 2021

    2.2K
    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

    1.5K
    Robotics in Surgery: A Modular Robotic Platform Driven Gastric Wedge Resection
    07:27

    Robotics in Surgery: A Modular Robotic Platform Driven Gastric Wedge Resection

    Published on: February 7, 2025

    950

    科学领域:

    • 医疗机器人 医疗机器人
    • 计算机视觉 计算机视觉
    • 图像细分 图像细分

    背景情况:

    • 准确的手术仪器细分对于安全和精确的微创手术机器人手术至关重要.
    • 现实世界中的手术场景带来了诸如闭塞,重叠的仪器和视觉噪音等挑战,阻碍了传统模型.

    研究的目的:

    • 开发一个先进的框架,SurgSeg-GAN,用于强大的手术仪器细分.
    • 在复杂的外科环境中提高细分的准确性和可靠性.

    主要方法:

    • 提出了SurgSeg-GAN,这是一个混合实例细分框架,将微调的YOLOv11模型与生成对抗网络 (GAN) 结合起来.
    • 该GAN组件旨在生成封闭口罩并恢复缺失的仪表功能.
    • 验证了EndoVis 2017和EndoVis 2018手术仪器细分数据集的框架.

    主要成果:

    • 在2017年EndoVis数据集中,SurgSeg-GAN实现了77%的平均跨欧盟交叉点 (mIoU) 和90%的Dice系数.
    • 在EndoVis 2018数据集中,该框架达到71%的mIoU和87%的Dice系数.
    • 超过了几种最先进的实例细分方法,证明了增强的稳定性和通用性.

    结论:

    • 在具有挑战性的条件下,SurgSeg-GAN显著提高了手术仪器细分精度.
    • 封闭感知GAN的集成使部分可见仪器的功能恢复成为可能.
    • 该框架有助于在机器人辅助手术中提供更安全,更可靠的实时指导.