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

相关实验视频

Updated: Feb 28, 2026

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
10:23

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

3.8K

基于深度学习的头部和部可变形图像记录,使用时空分析和自我注意力.

Donghoon Lee1, Yu-Chi Hu1, Teeradon TreeChairusame2,3

  • 1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

Physics and imaging in radiation oncology
|February 27, 2026
PubMed
概括

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

A Two-Stage Coarse-to-Fine Framework for Sparse Crowd Density Prediction in Digital Twin-Based Safety Monitoring.

Sensors (Basel, Switzerland)·2026
Same author

Limited Contribution of T<sub>1</sub> Relaxation to GluCEST MRI Signal Differences Across Four Rat Models with Distinct Pathophysiological Features.

Molecular imaging and biology·2026
Same author

Beyond auto-segmentation: the case for planning and dosimetry AI in head and neck radiation oncology.

BMJ oncology·2026
Same author

Early Salivary Gland Shrinkage Is Associated With an Increased Risk of Acute Xerostomia in Head and Neck Cancer Radiation Therapy.

Advances in radiation oncology·2026
Same author

Time-resolved GluCEST MRI of acute glutamate-related signal changes following kainic acid administration.

Journal of the neurological sciences·2026
Same author

SECmeres outperform extracellular vesicles as potential blood RNA biomarkers for Alzheimer's disease.

Nature communications·2026

一个新的深度学习算法为头癌放射治疗提供了快速准确的可变形图像注册,使实时适应性治疗规划成为可能.

科学领域:

  • 医疗成像医学成像
  • 辐射疗法 辐射疗法
  • 人工智能的人工智能

背景情况:

  • 头癌 (HNC) 放射治疗期间的解剖学变化需要适应性策略来准确的剂量输送.
  • 传统的可变形图像记录 (DIR) 方法对于在线适应性放射治疗 (ART) 工作流程来说太慢了.
  • 在HNC中纵向成像对精确的治疗适应提出了挑战.

研究的目的:

  • 开发和评估一种基于深度学习的DIR算法,用于纵向HNC成像.
  • 为了实现实时的快速准确的图像注册,ART.
  • 通过先进的图像注册,提高HNC放射治疗中的剂量递送精度.

主要方法:

  • 开发了一个基于补丁的深度学习模型,集成了3D CNN,自我注意力和ConvLSTM.
  • 该模型使用复合损失函数预测了双向变形向量场.
  • 60个HNC患者数据集 (pCT和每周的CBCT) 用于培训和测试,与LDDMM进行基准测试.

主要成果:

  • 深度学习DIR在不到3分钟的时间内实现了双向注册 (平均每患者30秒),比LDDMM快得多.
  • 该算法与LDDMM的准确性相匹配或超过,关键结构的子相似系数高于0.8.
  • 观察到改善的DVF一致性和减少的豪斯多夫距离,不需要手动参数调整.
关键词:
适应性放射疗法 适应性放射疗法深度学习是一种深度学习.可变形图像的注册方式头癌是头部和部的癌症.

更多相关视频

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

3.6K

相关实验视频

Last Updated: Feb 28, 2026

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
10:23

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

3.8K
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

3.6K

结论:

  • 拟议的DIR算法为HNC提供了快速,准确和一致的图像注册.
  • 这支持实时自适应性放射治疗工作流程和追溯剂量积累.
  • 该方法为HNC的个性化放射治疗提供了可行的解决方案.