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

Computed Tomography01:10

Computed Tomography

9.3K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
9.3K
Imaging Studies VII: Vascular Imaging01:19

Imaging Studies VII: Vascular Imaging

438
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...
438
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

536
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
536
Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

12.0K
Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
12.0K

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

Robust Construction of Diffusion MRI Atlases with Correction for Inter-Subject Fiber Dispersion.

Computational diffusion MRI : MICCAI Workshop·2017
Same author

Robust Fusion of Diffusion MRI Data for Template Construction.

Scientific reports·2017
Same author

Learning-Based Multimodal Image Registration for Prostate Cancer Radiation Therapy.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2017
Same author

Segmenting hippocampal subfields from 3T MRI with multi-modality images.

Medical image analysis·2017
Same author

Joint Discriminative and Representative Feature Selection for Alzheimer's Disease Diagnosis.

Machine learning in medical imaging. MLMI (Workshop)·2017
Same author

Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes.

IEEE transactions on medical imaging·2017
Same journal

Co-assistant networks by pathology foundation model and convolutional neural network for gigapixel whole slide image analysis.

Medical image analysis·2026
Same journal

MBAS2024: A large-scale benchmark for multi-class bi-atrial segmentation in multi-center contrast-enhanced MRIs.

Medical image analysis·2026
Same journal

Respiratory motion augmentation for personalized super-resolution (RMApSR) of 3D cine MR images in MRI-guided radiotherapy.

Medical image analysis·2026
Same journal

Biom3d, a modular framework to host and develop 3D segmentation methods.

Medical image analysis·2026
Same journal

Embracing intra-class heterogeneity for semi-supervised medical image segmentation: From diversity to precision.

Medical image analysis·2026
Same journal

Real-time patient-specific microwave ablation zone prediction via a unified bioheat solver and MRI-informed perturbation learning.

Medical image analysis·2026
查看所有相关文章

相关实验视频

Updated: Mar 12, 2026

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
07:15

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

3.5K

一个内容意识的可变速率框架用于病理学学习图像压缩 (PathoLIC).

Weiqi Li1, Yonghao Li1, Haoyuan Chen1

  • 1School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials & Devices, ShanghaiTech University, Shanghai, China.

Medical image analysis
|March 10, 2026
PubMed
概括
此摘要是机器生成的。

病理学学习图像压缩 (PathoLIC) 通过优先考虑诊断区域,提供高效的千兆像素整片图像压缩. 这种新的方法实现了超过8倍的压缩,同时保留了癌症研究的关键细节.

关键词:
学习了图像压缩.可变速率压缩的压缩方式.整个幻灯片图像 (WSI) 的一个整体.

更多相关视频

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

1.4K
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

49.6K

相关实验视频

Last Updated: Mar 12, 2026

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
07:15

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

3.5K
A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

1.4K
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

49.6K

科学领域:

  • 数字病理学数字病理学
  • 医学图像分析 医学图像分析
  • 机器学习 机器学习

背景情况:

  • 千兆像素整张幻灯片图像 (WSIs) 带来了重大的存储和计算挑战.
  • 由于忽视了补丁冗余和统一的压缩策略,目前的压缩方法是次优的.

研究的目的:

  • 介绍PathoLIC,一种基于学习的变速压缩框架,用于WSIs.
  • 通过整合内容意识和补丁关系压缩来解决现有方法的局限性.

主要方法:

  • 根据诊断相关性,PathoLIC将内容分数分配给WSI补丁.
  • 可变速率压缩优先考虑重要区域 (如瘤) 的细节,并压缩信息较少的区域 (如肌层).
  • 注意力机制捕捉了补丁间的关系,以最大限度地减少冗余.

主要成果:

  • 与标准的Aperio SVS格式相比,PathoLIC实现了超过8倍的压缩.
  • 保存了对诊断解释至关重要的图像细节.
  • 在下游任务中表现出强的表现,例如癌症亚型和细胞核细分.

结论:

  • 帕索利克为大规模WSI数据管理提供了有效的解决方案.
  • 能够有效地存储,传输和分析千兆像素病理图像.
  • 促进数字病理学研究和临床应用的进步.