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

Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

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

Case Report: The "atoll sign": a case series on an unusual radiological pattern of immune-mediated pneumonitis.

Frontiers in immunology·2026
Same author

Update of the MSKCC nomogram for metastatic progression and its role in active surveillance: the Italian TPCP cohort.

Frontiers in oncology·2026
Same author

Synthetic Implant Migration Generation for Accuracy and Precision Evaluation of AI-Based CT-RSA in Total Hip Arthroplasty.

Diagnostics (Basel, Switzerland)·2026
Same author

A FHIR Consent Profile for European Research Biobanks.

Studies in health technology and informatics·2026
Same author

A comprehensive European Colorectal Cancer Cohort dataset.

Scientific data·2026
Same author

Transperineal Versus Transrectal Biopsy for Prostate Cancer Diagnosis: A Systematic Review and Meta-analysis of Randomized Controlled Trials.

European urology oncology·2026

相关实验视频

Updated: Jul 12, 2026

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software
09:57

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Published on: December 16, 2014

13.1K

一个开源平台,用于结构化的注释和计算工作流程在数字病理学研究的数字病理学研究.

Luca Lianas1, Mauro Del Rio2, Luca Pireddu2

  • 1Visual and Data-intensive Computing, CRS4 (Center for Advanced Studies, Research and Development in Sardinia), Pula, Italy. luca.lianas@crs4.it.

Scientific reports
|August 7, 2025
PubMed
概括

数字病理学的进步使人工智能工具成为可能,但目前的注释方法缺乏结构. CRS4数字病理学平台 (CDPP) 为高质量,可重复的数字病理学研究数据集提供结构化,多标签的注释.

关键词:
附注协议 附注协议 附注协议计算病理学计算病理学计算的起源 计算的起源数字病理学数字病理学组织病理学 组织病理学在WSI注释中,注释为:

更多相关视频

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues
06:44

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues

Published on: March 29, 2021

2.6K
DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma
09:58

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

Published on: June 6, 2025

467

相关实验视频

Last Updated: Jul 12, 2026

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software
09:57

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Published on: December 16, 2014

13.1K
Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues
06:44

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues

Published on: March 29, 2021

2.6K
DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma
09:58

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

Published on: June 6, 2025

467

科学领域:

  • 数字病理学数字病理学
  • 计算病理学计算病理学
  • 医疗信息学医学信息学

背景情况:

  • 数字病理学使人工智能工具和临床研究的大规模数据采集成为可能.
  • 目前用于全幻灯片图像 (WSI) 的开源注释工具使用单一标签,限制结构化数据表示.
  • 在注释中缺乏协议遵守和来源跟踪,导致数据变化和可复制性降低.

研究的目的:

  • 引入CRS4数字病理学平台 (CDPP),这是一个开源系统,用于WSI的结构化注释.
  • 通过实现多标签,协议驱动的数据收集,解决当前注释工具的局限性.
  • 提高数字病理学数据集的质量,可重复性和可重复使用性,用于研究.

主要方法:

  • 开发CRS4数字病理学平台 (CDPP),提供结构化,多标签注释的功能.
  • 实现可定制的注释协议和专用工具,以提高准确性和效率.
  • 集成基于工作流的计算分析与来源跟踪.

主要成果:

  • CDPP支持结构化,多标签的形态和临床图像注释.
  • 该平台促进了对可控制,可定制的注释协议的遵守.
  • 集成的来源跟踪确保工作流的可复制性和数据的可重复使用性.
  • 在三个不同的研究中成功申请,证明了强大的性能.

结论:

  • CDPP有效地帮助病理学家生成高质量,结构化的注释数据集.
  • 该平台提高了数字病理学注释过程中的准确性,效率和一致性.
  • CDPP促进数字病理学研究数据的可重复性和可重复使用性.