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

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

6.8K
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
6.8K

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

Interpolation and Imputation Strategies for Missing Segments in Continuous Pressure-Flow Cerebral Bio-Signals: A Systematic Scoping Review.

Sensors (Basel, Switzerland)·2026
Same author

Comparative analysis of processed EEG indices and entropy-based metrics for assessing anesthetic depth: a scoping review - PRISMA-ScR.

BMC biomedical engineering·2026
Same author

Detection of regional disparity in cerebrovascular reactivity using a custom whole brain functional near-infrared spectroscopy based mapping system: A prospective observational study.

PLOS digital health·2026
Same author

HACR-Net: An Efficient hybrid attention network for MRI image super-resolution.

PloS one·2026
Same author

A novel algorithm for the continuous determination of individualized intracranial pressure (iICP) thresholds using a multi-window weighted approach.

BMC medical informatics and decision making·2026
Same author

Algorithmic derivation of optimal CPP, MAP, and BIS targets from cerebrovascular reactivity indices: a systematic scoping review.

Physiological measurement·2026

相关实验视频

Updated: May 24, 2025

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

929

立体内镜图像超分辨率和手术仪器细分的SEGSRNet.

Mansoor Hayat, Supavadee Aramvith, Titipat Achakulvisut

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    概括

    SEGSRNet 改进了在低分辨率内镜图像中使用超分辨率进行细分之前的手术仪器识别. 这提高了准确性,以获得更好的外科结果和更好的患者护理.

    科学领域:

    • 医疗成像医学成像
    • 计算机视觉 计算机视觉
    • 机器人手术 机器人手术

    背景情况:

    • 低分辨率的立体内镜图像对准确的手术仪器识别构成了挑战.
    • 精确的工具识别对于提高手术准确性和微创手术患者安全至关重要.

    研究的目的:

    • 引入SEGSRNet,这是一个新的框架,用于提高低分辨率立体内镜图像中的外科器械细分.
    • 通过在细分之前整合超分辨率技术来提高图像清晰度和细分精度.

    主要方法:

    • 在分割之前,SEGSRNet使用最先进的超高分辨率来提高图像质量.
    • 该框架集成了先进的特征提取,注意力机制和空间处理,用于详细的图像利.
    • 用标准评估指标对超分辨率和细分任务进行现有模型的比较分析.

    主要成果:

    • 在超分辨率任务中,SEGSRNet表现出卓越的性能,由高峰信号对噪声比率 (PSNR) 和结构相似性指数测量 (SSIM) 得分所证明.
    • 该模型实现了改进的细分精度,通过更高的交叉与联盟 (IoU) 和子得分指标来表示.
    • 增强的图像分辨率和精确的细分能力得到了验证.

    结论:

    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

    2.6K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    348

    相关实验视频

    Last Updated: May 24, 2025

    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

    929
    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

    2.6K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    348
    • SEGSRNet有效地解决了用于手术仪器识别的低分辨率内镜成像的局限性.
    • 拟议的框架为改善手术准确性和患者护理结果提供了巨大的潜力.
    • SEGSRNet代表了应用深度学习在机器人手术中进行增强的医学图像分析的进步.