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

Leukaemic stem cell burden enhances prognostic precision in the non-adverse European LeukemiaNet risk group of acute myeloid leukaemia.

British journal of haematology·2026
Same author

Corrigendum to "Enhanced prognostic score for breast cancer brain metastases incorporating contemporary clinical features and leptomeningeal disease: development and validation" [The Breast 88 (2026) 104826].

Breast (Edinburgh, Scotland)·2026
Same author

Reply to Takahashi.

Endoscopy·2026
Same author

α-Klotho as a central integrative signalling hub in cognitive function and neuroprotection in neurodegenerative diseases.

Metabolic brain disease·2026
Same author

Agentic Artificial Intelligence for the Automated Generation of Accurate Summary Podcasts of Radiology Research Papers.

Korean journal of radiology·2026
Same author

Enhanced prognostic score for breast cancer brain metastases incorporating contemporary clinical features and leptomeningeal disease: Development and validation.

Breast (Edinburgh, Scotland)·2026

相关实验视频

Updated: Jan 8, 2026

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

3.7K

区域意识和序列信息化的多解码器网络,用于在多参数MRI中进行强大的脑质瘤细分.

Abbas Mohamed Rezk1, Abdulkhalek Al-Fakih1, Abdullah Shazly1

  • 1Department of Artificial Intelligence and Data Science, College of Artificial Intelligence Convergence, Sejong University, Seoul, Republic of Korea.

Computers in biology and medicine
|December 18, 2025
PubMed
概括

这项研究引入了一个新的深度学习框架,用于从MRI扫描中精确地细分质母细胞瘤. 该方法增强了瘤亚区域的划分,改善了神经瘤学的诊断和治疗计划.

关键词:
大脑瘤的细分 脑瘤的细分多个路径的多个路径.区域意识 - 地区意识根据序列信息的序列信息.

更多相关视频

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.4K
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

49.2K

相关实验视频

Last Updated: Jan 8, 2026

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

3.7K
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.4K
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

49.2K

科学领域:

  • 神经瘤学神经瘤学
  • 医学图像分析 医学图像分析
  • 人工智能在医学中的应用

背景情况:

  • 通过多参数MRI精确地细分质母细胞瘤亚区域对于患者的护理至关重要.
  • 由于异质成像特征,在划分瘤子区域方面存在挑战.
  • 当前的深度学习模型通常不充分利用单个MRI序列的临床特异性.

研究的目的:

  • 开发一个新的深度学习框架,用于准确和强大的质母细胞瘤子区域细分.
  • 为了改善增强瘤,非增强瘤核心和周围胀的划分.
  • 提高跨不同数据集的细分模型的通用性.

主要方法:

  • 一个多解码器深度学习架构,用于关键瘤子区域的独立细分.
  • 一个基于序列的指导策略,以使MRI序列与特定的诊断目标保持一致.
  • 一个改进的自我注意力机制,以改善特征重新校准和解剖学连贯性.

主要成果:

  • 在BraTS 2023数据集上获得了0.9009的平均子相似系数 (DSC) 和6.61毫米的HD95.
  • 超越了最先进的方法,特别是在增强瘤细分方面.
  • 在四个外部数据集中表现出强大的概括性,在具有挑战性的场景中,DSC增长高达4.09%.

结论:

  • 拟议的框架为质母细胞瘤细分提供了强大的和可通用的解决方案.
  • 临床洞察力和方法创新的整合提高了细分精度.
  • 支持在神经瘤学中改进个性化治疗规划和结果评估.