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

Image quality improvement in single plane-wave imaging using deep learning.

Ultrasonics·2024
Same author

Neural Radiance Field-Inspired Depth Map Refinement for Accurate Multi-View Stereo.

Journal of imaging·2024
Same author

Future behaviours decision-making regarding travel avoidance during COVID-19 outbreaks.

Scientific reports·2022
Same author

Clinical features of moyamoya disease with Graves' disease: a retrospective study of 394,422 patients with thyroid disease.

Endocrine journal·2022
Same author

Correction: Encapsulated Angioinvasive Follicular Thyroid Carcinoma: Prognostic Impact of the Extent of Vascular Invasion.

Annals of surgical oncology·2022
Same author

Impact of Local Control on Clinical Course in Stage IVC Anaplastic Thyroid Carcinoma.

World journal of surgery·2022

相关实验视频

Updated: Jul 8, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.9K

使用CNN和RNN与图像重建损失的超声波探针的姿势估计.

Kanta Miura, Koichi Ito, Takafumi Aoki

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    概括

    精确的3D超声波 (美国) 图像重建需要精确的探针姿势估计. 我们的CNN方法,经过图像重建损失的训练,实现了对1D数组探针的高效探针姿势估计.

    科学领域:

    • 医疗成像医学成像
    • 计算机视觉 计算机视觉
    • 机器学习 机器学习

    背景情况:

    • 从1D阵列探测序列中精确的3D超声波 (美国) 图像重建是具有挑战性的.
    • 精确估计探测器的姿势 (位置和方向) 对于高准确度的3D美国成像至关重要.
    • 传统的方法往往难以达到复杂的美国扫描所需的精度.

    研究的目的:

    • 开发一种使用卷积神经网络 (CNN) 进行准确探针姿势估计的新方法.
    • 为了训练CNN模型,使用图像重建损失函数来提高姿势准确性.
    • 通过对现有的3D美国图像重建技术来验证拟议方法的性能.

    主要方法:

    • 一个卷积神经网络 (CNN) 架构被设计用于探针姿势估计.
    • 美国有线电视新闻网 (CNN) 使用图像重建损失的训练,由专门的网络计算.
    • 重建网络使用了编码解码器结构来生成中间的美国图像.

    主要成果:

    • 拟议的基于CNN的方法证明了高效的探针姿势估计能力.
    • 实验结果显示,与传统的探头姿势估计方法相比,其性能优越.
    • 图像重建损失有效地引导了CNN的训练,以准确地确定姿势.

    更多相关视频

    An Experimental Protocol for Assessing the Performance of New Ultrasound Probes Based on CMUT Technology in Application to Brain Imaging
    16:01

    An Experimental Protocol for Assessing the Performance of New Ultrasound Probes Based on CMUT Technology in Application to Brain Imaging

    Published on: September 24, 2017

    10.5K
    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

    相关实验视频

    Last Updated: Jul 8, 2025

    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
    04:23

    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

    Published on: April 21, 2023

    1.9K
    An Experimental Protocol for Assessing the Performance of New Ultrasound Probes Based on CMUT Technology in Application to Brain Imaging
    16:01

    An Experimental Protocol for Assessing the Performance of New Ultrasound Probes Based on CMUT Technology in Application to Brain Imaging

    Published on: September 24, 2017

    10.5K
    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

    结论:

    • 开发的CNN方法为3D超声波中准确的探针姿势估计提供了有效的解决方案.
    • 用图像重建损失进行训练是提高姿势估计准确性的可行策略.
    • 这种方法为使用1D阵列探针进行高质量的3D美国图像重建提供了有希望的进步.