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

Imaging Studies for Cardiovascular System IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

542
Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
542

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

Pyrethrin II Impairs Mitochondrial Potential through ROS and MAPK Pathways in HT‑22 Cells.

Neurotoxicology·2026
Same author

Volatile methylsiloxanes in a textile dyeing and printing industrial park: Source profiles, ambient distributions and SOA formation potential.

Journal of hazardous materials·2026
Same author

Efficacy of probiotic and synbiotic supplementation on metabolic and endocrine parameters in polycystic ovary syndrome: a meta-analysis of randomized controlled trials.

BMC endocrine disorders·2026
Same author

Ultrathin organic crystalline FET sensor based on high-capacitance hydrophobic nanocellulose insulating layer for real-time on-site detection of volatile basic nitrogen.

Food chemistry·2026
Same author

Eliminate the Metal Ion in the Edible Oil Based on High Extraction pH-Switchable Deep Eutectic Solvents.

ChemPlusChem·2026
Same author

Chemoenzymatic Synthesis of 6-Sulfo Lewis X-Related Glycans for Probing Their Ligand-Binding Proteins.

Journal of the American Chemical Society·2026

相关实验视频

Updated: May 6, 2026

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

3.3K

DBCM-net:用于医疗图像细分的双脊柱级联多卷积细分网络.

Xiuwei Wang1, Biyuan Li1,2, Jinying Ma1

  • 1School of Electronic Engineering, Tianjin University of Technology and Education, Tianjin, 300222, People's Republic of China.

Biomedical physics & engineering express
|September 17, 2025
PubMed
概括

本研究介绍了双脊柱级联多卷积细分网络 (DBCM-Net),用于准确的医疗图像细分. DBCM-Net克服了现有模型的局限性,在细分具有挑战性的内镜和皮肤镜图像方面实现了卓越的性能.

关键词:
在美国,CNN是CNN.马巴马巴 马巴 马巴 马巴 马巴医疗图像细分 医疗图像细分变压器的变压器是一个变压器.

更多相关视频

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

736

相关实验视频

Last Updated: May 6, 2026

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

3.3K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

736

科学领域:

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

背景情况:

  • 医学图像细分对于临床应用至关重要,但在内镜/皮肤镜图像中面临着低对比度和模糊边界的挑战.
  • 现有的单个和双编码器架构在细节保存和上下文集成方面扎,导致细分不准确.

研究的目的:

  • 开发一个先进的细分网络,双脊柱级联多卷积细分网络 (DBCM-Net),以解决医疗图像细分方面的局限性.
  • 提高细节的准确性和保存在细分具有挑战性的医疗图像,特别是来自内镜和皮肤镜来源的图像.

主要方法:

  • 采用级联式双编码器架构,采用多轴视觉变压器和Vision Mamba编码器进行多尺度特征提取.
  • 引入了全球和本地融合注意区块 (GLFAB) 和深度可分离的卷积注意模块 (DSCAM),以增强特征表示和集成.
  • 使用特征精制融合块 (FRFB) 在级结构内精制特征地图.

主要成果:

  • 在CVC-ClinicDB上获得了94.93%的高子系数,在ISIC2018上获得了91.93%的高子系数,在ACDC上获得了92.73%.
  • 与最先进的方法相比,在多个医疗图像细分数据集中表现出卓越的性能.
  • 展示了细粒度边缘细节在分割图像中的有效保存.

结论:

  • DBCM-Net为精确的医疗图像细分提供了一个强大的解决方案,优于现有的方法.
  • 拟议的架构有效地整合了多个规模的功能和注意力机制,以提高细分精度.
  • DBCM-Net显示了需要高保真度医疗图像细分的临床应用的巨大潜力.