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

CroCoDeEL: accurate control-free detection of cross-sample contamination in metagenomic data.

Nature communications·2026
Same author

StrainMake: reproducible hybrid metagenomics with MAG recovery and strain-level resolution.

Bioinformatics (Oxford, England)·2026
Same author

A gut microbiome-kidney-heart axis predictive of future cardiovascular diseases.

Nature communications·2026
Same author

Validation of a novel semi-automated ECG quantification tool, applied to a cardio-oncology : Semi-automated ECG Tool applied to cardio-oncology.

Cardio-oncology (London, England)·2025
Same author

Prominent mediatory role of gut microbiome in the effect of lifestyle on host metabolic phenotypes.

Gut microbes·2025
Same author

Transgender-Affirming Hormone Therapies, QT Prolongation, and Cardiac Repolarization.

JAMA network open·2025

相关实验视频

Updated: Jan 7, 2026

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells
06:03

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

791

植物SAM:一个对象检测驱动的分段管道,用于草本馆标本.

Youcef Sklab1, Florian Castanet1, Hanane Ariouat1

  • 1Institut de Recherche pour le Développement (IRD) Sorbonne Université, UMMISCO Paris France.

Applications in plant sciences
|December 31, 2025
PubMed
概括

植物SAM,一个自动化细分管道,通过消除背景噪声来改善草本图像分类. 这种深度学习方法提高了植物特征识别的准确性.

关键词:
分段任何模型 (SAM)这就是YOLOv10的意义.植物学分析 植物学分析草药馆标本的样本.语义细分 语义细分 语义细分 语义细分

更多相关视频

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

25.0K
Robust DNA Isolation and High-throughput Sequencing Library Construction for Herbarium Specimens
13:03

Robust DNA Isolation and High-throughput Sequencing Library Construction for Herbarium Specimens

Published on: March 8, 2018

11.0K

相关实验视频

Last Updated: Jan 7, 2026

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells
06:03

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

791
From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

25.0K
Robust DNA Isolation and High-throughput Sequencing Library Construction for Herbarium Specimens
13:03

Robust DNA Isolation and High-throughput Sequencing Library Construction for Herbarium Specimens

Published on: March 8, 2018

11.0K

科学领域:

  • 植物学 植物学
  • 计算机科学 计算机科学
  • 数字成像技术的数字成像.

背景情况:

  • 使用深度学习的Herbarium图像分类面临着由于异质背景的挑战.
  • 背景噪音和文物可以误导模型并降低分类准确性.

研究的目的:

  • 开发一个自动化细分管道,PlantSAM,用于增强草本图像分析.
  • 为了提高基于深度学习的植物特征从草本馆图像的分类的准确性.

主要方法:

  • 植物SAM集成了YOLOv10用于对象检测和分段任何模型 (SAM2) 进行细分.
  • 无论是YOLOv10还是SAM2,都在草本馆图像上进行了微调,YOLOv10为SAM2提供了界限框提示.
  • 使用交叉与联盟 (IoU) 和索伦森-迪斯系数来评估性能.

主要成果:

  • 植物SAM实现了最先进的细分性能,IOU为0.94和Sørensen-Dice系数为0.97.
  • 将细分图像集成到分类模型中,提高了五种植物特征的性能.
  • 精度提升达到了4.36%,F1的得分提升达到了4.15%.

结论:

  • 删除背景对于改进草本库图像分析至关重要.
  • 自动化细分增强了深度学习模型专注于植物结构的能力,从而获得更好的分类结果.