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

Imaging Studies I: Kidney, Ureter, and Bladder Studies01:28

Imaging Studies I: Kidney, Ureter, and Bladder Studies

Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...
Imaging Studies V: Intravenous Urography and Retrograde Pyelography01:22

Imaging Studies V: Intravenous Urography and Retrograde Pyelography

IntroductionIntravenous Urography (IVU) and Retrograde Pyelography (RP) are important diagnostic imaging techniques used to evaluate the urinary system. These methods help identify structural abnormalities, obstructions, and functional issues in the kidneys, ureters, and bladder. Both procedures use iodine-based contrast media to enhance the visibility of urinary tract structures on X-ray images, though they differ in their methods and indications.1. Intravenous Urography (IVU)Intravenous...
Imaging Studies VI: Voiding Cystourethrography and Cystography01:22

Imaging Studies VI: Voiding Cystourethrography and Cystography

Voiding Cystourethrography (VCUG) and Cystography are specialized radiographic procedures used to examine the structure and function of the bladder and urethra.Voiding Cystourethrography (VCUG)A Voiding Cystourethrogram (VCUG) is a diagnostic imaging procedure that assesses the anatomy and function of the lower urinary tract. It focuses on the bladder, bladder neck, and urethra, helping detect abnormalities such as vesicoureteral reflux (VUR)—the backward or reverse flow of urine into the...
Imaging Studies VII: Vascular Imaging01:19

Imaging Studies VII: Vascular Imaging

DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...

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

Recursive variational autoencoders for 3D blood vessel generative modeling.

Medical image analysis·2025
Same author

Enhancing and advancements in deep learning for melanoma detection: A comprehensive review.

Computers in biology and medicine·2025
Same author

Improving realism in abdominal ultrasound simulation combining a segmentation-guided loss and polar coordinates training.

Medical physics·2025
Same author

Dynamical organization of vimentin intermediate filaments in living cells revealed by MoNaLISA nanoscopy.

Bioscience reports·2025
Same author

Chylomicron Characteristics Are Associated With Microsomal Triglyceride Transfer Protein in an Animal Model of Diet-Induced Dysbiosis.

Journal of lipid and atherosclerosis·2025
Same author

Transient frequency preference responses in cell signaling systems.

NPJ systems biology and applications·2024

相关实验视频

Updated: May 10, 2026

Automated Multiplex Immunofluorescence Panel for Immuno-oncology Studies on Formalin-fixed Carcinoma Tissue Specimens
10:49

Automated Multiplex Immunofluorescence Panel for Immuno-oncology Studies on Formalin-fixed Carcinoma Tissue Specimens

Published on: January 21, 2019

21.4K

瑞瓦:一组图像数据集的传统的巴氏涂抹细胞学与多个独立的注释.

Paula Perez Bianchi1, Sol Anselmo2, Malena Vásquez Currié2

  • 1Departamento de Computación (DC), Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (UBA), Buenos Aires, Argentina.

Scientific data
|December 9, 2025
PubMed
概括

这项研究引入了RIVA,这是用于宫癌查的传统巴氏涂片图像的新数据集. 高质量的,专家注释的数据集支持人工智能开发,以改善诊断.

更多相关视频

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

4.4K
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

752

相关实验视频

Last Updated: May 10, 2026

Automated Multiplex Immunofluorescence Panel for Immuno-oncology Studies on Formalin-fixed Carcinoma Tissue Specimens
10:49

Automated Multiplex Immunofluorescence Panel for Immuno-oncology Studies on Formalin-fixed Carcinoma Tissue Specimens

Published on: January 21, 2019

21.4K
Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

4.4K
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

752

科学领域:

  • 医疗成像医学成像
  • 计算病理学计算病理学
  • 在瘤学瘤学.

背景情况:

  • 传统的巴氏涂抹对于宫癌查至关重要,特别是在资源较少的环境中.
  • 现有的图像数据集主要使用基于液体的制剂,限制了对传统涂抹的AI开发.
  • 需要高质量的,注释的数据集传统的巴氏涂片图像.

研究的目的:

  • 介绍RIVA,一种新的,高分辨率的图像数据集,用于传统的巴氏涂抹.
  • 为培训和评估用于宫癌检测的人工智能模型提供强大的基础真相.
  • 为了使在宫细胞学分类中能够分析注释器间的一致性.

主要方法:

  • 从115名患者中收集了959张常规巴氏涂片图像 (1024x1024 px) 在40倍放大.
  • 使用贝塞斯达分类系统的注释图像,最多有四名独立的医疗专业人员.
  • 详细的注释包括15949个独特细胞的核坐标和分类标签.

主要成果:

  • RIVA数据集包括八个贝塞斯达类别的26,158个注释,包括癌前病变 (SCC,HSIL,ASCH,LSIL,ASCUS) 和非病变类型 (NILM,ENDO,INFL).
  • 实现了高的注释者间协议:94%的损伤与非损伤和74%的整个八类计划.
  • 该数据集为人工智能模型开发提供了基于共识的基本真理.

结论:

  • 瑞瓦 (RIVA) 是一个有价值的资源,用于推进人工智能在宫癌查使用传统的巴氏涂抹.
  • 数据集的质量和详细的注释促进了强大的AI培训和验证.
  • 高度的注释者间协议验证了数据集对AI开发和研究的可靠性.