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

Fast MRI of bones in the knee - An AI-driven reconstruction approach for adiabatic inversion recovery prepared ultra-short echo time sequences.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)·2026

Same author

Overall Survival with [<sup>177</sup>Lu]Lu-PSMA-617 Versus [<sup>177</sup>Lu]Lu-PSMA I&T: A Propensity Score-Matched Real-World Analysis.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine·2026

Same author

Direct Androgen Receptor Antagonism Enhances Therapeutic PSMA Radioligand Uptake in Prostate Cancer Models.

Molecular imaging and biology·2026

Same author

CXCR4-targeted PET/CT in early systemic sclerosis-associated interstitial lung disease: a prospective proof-of-concept study of in vivo inflammatory activity.

European journal of nuclear medicine and molecular imaging·2026

Same author

Quantitative accuracy of <sup>177</sup>Lu SPECT/CT imaging using ring-shaped CZT versus dual-head NaI systems.

EJNMMI physics·2026

Same author

Factors Influencing Double-Strand Break Focus Biodosimetry for Detection of Internal Low-LET Irradiation in Human Blood Samples.

Radiation research·2026

Search research articles

相关实验视频

Updated: Jul 17, 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

PSMA-PET 改进了基于深度学习的自动化CT脏细分.

Julian Leube¹, Matthias Horn¹, Philipp E Hartrampf¹

¹University Hospital Würzburg, Department of Nuclear Medicine, Oberdürrbacher Str. 6, 97080 Würzburg, Germany.

Zeitschrift fur medizinische Physik

|September 4, 2023

概括

结合基于PET的预细分可以显著改善基于深度学习的脏细分,用于放射性药物治疗剂量测量. 这种方法提高了准确性,特别是对于复杂的脏解剖学,减少了计算时间.

关键词:

人工智能的人工智能是人工智能.自动细分系统自动细分系统基于深度学习的CT细分基于深度学习的CT细分细分的细分是指的细分.在PET/CT成像中使用PET/CT成像

更多相关视频

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

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

Published on: December 19, 2020

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

相关实验视频

Last Updated: Jul 17, 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

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

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

Published on: December 19, 2020

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

科学领域:

医疗成像医学成像
放射性药物疗法是一种放射性药物疗法.
人工智能在医学中的应用

背景情况:

在放射性药物治疗中,准确的剂量测量需要精确的器官细分,脏是危险的关键器官.
手动脏细分是耗时的;使用CT数据的深度学习方法已被开发用于自动化细分.
以前的自动化方法仅依赖于CT,这促使人们对将PSMA-PET数据纳入的附加值进行了调查.

研究的目的:

评估将PSMA-PET数据集成到用于自动细分的深度学习模型的影响.
将仅CT细分的性能与包含基于PET信息的方法进行比较.

主要方法:

开发并测试了五种基于U-Net的深度学习方法,用于自动细分,使用来自108次PET/CT检查的CT和/或PET数据.
包括仅使用CT,仅使用PET,结合CT和PET,使用粗皮PET面罩CT和使用PET面罩CT预分段的方法.
定量评估涉及20名患者的测试组中的Dice得分,体积偏差和豪斯多夫距离,核医生对另外100名患者进行视觉评估.

主要成果:

使用基于PET的粗面膜预分段的CT图像实现了最佳性能.
这种PET增强方法显著优于仅CT细分,视觉评估证实了这一点.
核医生在80%的病例中更喜欢使用基于PET的预细分的细分.

结论:

整合基于PET的预细分可以大大提高基于深度学习的细分精度.
这种方法对患有囊的脏或与其他器官相邻的脏特别有益,在具有挑战性的情况下可以改善细分.
这一进步有望缩短剂量计计算时间,提高放射性药物治疗的整体准确性.