Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

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

Development and assessment of an assisted diagnosis model using machine learning for identifying adult-onset Still's disease in fever of unknown origin: a retrospective study in China.

RMD open·2026

Same author

Integrating ultrasound-CT-MR for preoperative multi-task prediction in ovarian cancer: achieving diagnostic parity with multidisciplinary team consensus.

NPJ digital medicine·2026

Same author

Heterostructured NiFe-MOC/Ni<sub>3</sub>Fe/Ni<sub>4</sub>N for Photothermal-Promoted Anion Exchange Membrane Water Electrolysis.

Small (Weinheim an der Bergstrasse, Germany)·2026

Same author

Enhancing Predictive Healthcare Models through Counterfactual Data Augmentation: A Novel Approach Leveraging Counterfactual Generative Adversarial Networks.

IEEE journal of biomedical and health informatics·2026

Same author

Long-term reversal of Duchenne muscular dystrophy via circular arRNA-guided exon skipping in monkeys and humans.

Cell·2026

Same author

Heat stress variation exerts a more pronounced effect than vapor pressure deficit on maize yield in Northeast China.

International journal of biometeorology·2026

Search research articles

相关实验视频

Updated: Jul 8, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

基于局部特征增强的部分标签多器官细分.

Yanxia Zhao, Peijun Hu, Jingsong Li

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

概括

这项研究引入了一个新的3D网络,以改善CT扫描中的腹部器官的自动细分,解决部分标记医疗数据的挑战,以便更好地进行手术规划.

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:

从CT图像中对腹部器官进行自动细分对于手术规划至关重要.
不同医疗机构对器官的部分注释对多中心研究构成了重大挑战.

研究的目的:

开发一个强大的3D本地功能增强的多头细分网络.
为了解决多中心腹部多器官细分中的部分注释问题.

主要方法:

一个新的架构结合了全球分支 (3D Transformer和U-Net融合 - 3D TransUNet) 和本地3D U-Net分支.
当地分支机构以额外的腹部器官结构信息增强了全球分支机构.
对四个公开的CT数据集进行评估,具有不同的部分标签.

主要成果:

获得了93.01%的平均子得分系数 (DSC).
获得的平均豪斯多夫距离 (HD) 为3.489mm.
与三种最先进的方法相比,证明了更高的准确性和稳定性.

结论:

拟议的3D本地功能增强的多头细分网络有效地处理部分标记的数据集.

更多相关视频

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

相关实验视频

Last Updated: Jul 8, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

这种方法为多中心腹部多器官细分提供了更高的准确性和稳定性.

该方法显示了改善腹部疾病手术规划的巨大潜力.