Jove
Visualize
联系我们

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 journal

Characterization of genomic diversity in bacteriophages infecting Rhodococcus.

PloS one·2026
Same journal

Effectiveness of the Responding to Experienced and Anticipated Discrimination (READ) training on reducing stigma for medical students in Tunisia.

PloS one·2026
Same journal

Cell-cell junction gene signatures as subtype-specific prognostic biomarkers in breast cancer.

PloS one·2026
Same journal

GC-MS based tentative identification of γ-sitosterol from Brassica nigra seeds and evaluation of its anticancer potential: An integrated in vitro and in silico study.

PloS one·2026
Same journal

Ad-based social media interventions increase belief accuracy and generate pro-social opinions among non-news readers.

PloS one·2026
Same journal

Negotiating knowledge: The role of network hedging in the production of high-impact science.

PloS one·2026
查看所有相关文章
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

相关实验视频

Updated: Jul 1, 2025

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

MIS-Net:基于深度学习的CT图像多类细分模型.

Huawei Li1, Changying Wang1

  • 1College of Computer Science and Technology, Qingdao University, Qingdao City, China.

PloS one
|March 13, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了MIS-Net,这是一种用于准确医疗图像细分的深度学习模型. MIS-Net提高了肺部和肝脏细分的准确性,克服了噪音较大的CT扫描中传统方法的局限性.

更多相关视频

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.8K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

396

相关实验视频

Last Updated: Jul 1, 2025

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
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.8K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

396

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 传统的CT图像细分由于对比度低和噪音高而面临准确性挑战.
  • 现有的深度学习模型与精确的边缘细分和像素分类错误作斗争.

研究的目的:

  • 为改善CT图像细分提出医疗图像细分网 (MIS-Net) 模型.
  • 为了提高医疗图像中肺部和肝脏边缘细分的准确性.

主要方法:

  • 开发了MIS-Net,这是一个具有对称编码解码结构的深度学习模型.
  • 集成的多尺度心腔卷积用于从CT图像中全面的多尺度特征提取.

主要成果:

  • 在COVID-19CT肺部和感染细分数据集上,左和右肺部细分的子相似系数 (DSC) 为97.61%.
  • 在2017年肝癌细分挑战数据集上,肝脏细分的DSC达到98.78%.

结论:

  • MIS-Net有效地解决了传统和基于深度学习的CT图像细分方面的局限性.
  • 该模型在细分肺部和肝脏区域方面表现出高准确性,经过公开数据集的验证.