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

Preoperative cardiovascular evaluation in patients with cancer.

Frontiers in cardiovascular medicine·2026
Same author

Early anthracycline cardiotoxicity in adolescents and young adults with sarcoma: a prospective echocardiographic study.

ESC heart failure·2026
Same author

Defining Cardiovascular Endpoints in Oncology Trials: Challenges and Opportunities: A Scientific Statement From the American Heart Association.

Circulation·2026
Same author

Defining Cardiovascular Endpoints in Oncology Trials: Challenges and Opportunities: A Scientific Statement From the American Heart Association.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026
Same author

QT monitoring in chemotherapy.

Frontiers in cardiovascular medicine·2026
Same author

Targeted autonomic testing for radiation‑induced baroreflex failure in head and neck cancer survivors: index case and early program experience.

Cardio-oncology (London, England)·2026

相关实验视频

Updated: Jul 7, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.2K

基于深度学习的心脏基结构的自动细分用于肺癌.

Xinru Chen1, Raymond P Mumme2, Kelsey L Corrigan3

  • 1Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, United States.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
|December 20, 2023
PubMed
概括

这项研究开发了一种人工智能模型,用于肺癌患者的心脏亚结构的精确细分,从而提高了辐射安全. 深度学习方法实现了高准确性,其中94%的细分被认为是临床上可接受的.

关键词:
自动细分可以实现.冠状动脉的冠状动脉肺癌是一种肺癌.神经网络的神经网络的神经网络辐射疗法 辐射疗法

更多相关视频

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

505
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.5K

相关实验视频

Last Updated: Jul 7, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.2K
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

505
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.5K

科学领域:

  • 放射治疗和医学成像技术
  • 医疗保健中的人工智能
  • 心血管解剖学 心血管解剖学

背景情况:

  • 精确的心脏基结构细分对于在肺癌放射治疗期间最大限度地减少辐射诱导的心脏病至关重要.
  • 目前的手动细分方法耗时,容易引起观察者之间的变化.

研究的目的:

  • 开发和验证基于深度学习的自动细分模型,用于19个心脏基结构.
  • 评估人工智能驱动的心脏基结构划分的准确性和临床可接受性.

主要方法:

  • 在100名非小细胞肺癌患者中训练了一种nnU-Net自细分模型,这些患者手工划分心脏基结构.
  • 用42名患者的独立数据集上使用子相似系数 (DSC) 和剂量指标评估模型性能.
  • 通过四名医生的主观评估,评估了自分割轮的临床可接受性.

主要成果:

  • 人工智能模型实现了主要心脏结构的高平均DSC (例如,整个心脏0.95,腔室0.91).
  • 基结构的平均/最大剂量的平均绝对误差处于可接受的临床范围内.
  • 94%的自我细分轮被医生评为临床上可以接受的.

结论:

  • 开发的nnU-Net模型有效地以高精度划分心脏亚体结构,包括冠状动脉.
  • 这种人工智能方法在改善肺癌患者对心脏基底结构的辐射剂量评估方面显著有前途.
  • 该模型可以通过精确的自我细分来帮助降低辐射引起的心脏病的风险.