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

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...
Methods of Obtaining Topography01:25

Methods of Obtaining Topography

Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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...

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

Corrigendum to "Oculomics and AI: The eye as a biomarker for health span" [Asia-Pac J Ophthalmol 15 (1) (2026) 100282].

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)·2026
Same author

Reticular Pseudodrusen and Cardiovascular Disease or Related Mortality in AREDS and AREDS2.

JAMA ophthalmology·2026
Same author

Vision-Related Quality of Life in Geographic Atrophy: Association with Topographic Lesion Distribution.

Ophthalmology·2026
Same author

Improving Inter-Rater Reliability in Radiographic Edema Scoring in Acute Respiratory Failure Through Structured Training and Expert Feedback.

ATS scholar·2026
Same author

Simultaneous Segmentation of Geographic Atrophy in Longitudinally Acquired Fundus Autofluorescence Images.

Ophthalmology science·2026
Same author

ASSOCIATIONS BETWEEN SLEEP DISORDERS AND AGE-RELATED MACULAR DEGENERATION: A Systematic Review and Meta-Analysis.

Retina (Philadelphia, Pa.)·2026

相关实验视频

Updated: Jul 19, 2026

Thinned-skull Cortical Window Technique for In Vivo Optical Coherence Tomography Imaging
07:28

Thinned-skull Cortical Window Technique for In Vivo Optical Coherence Tomography Imaging

Published on: November 19, 2012

15.1K

深度学习方法使用三维OCT成像来预测地理缩进展.

Kenta Yoshida1, Neha Anegondi2, Adam Pely3

  • 1Clinical Pharmacology, Genentech, Inc., South San Francisco, CA, USA.

Translational vision science & technology
|February 6, 2025
PubMed
概括

使用3D光学连贯断层扫描 (OCT) 图像的深度学习模型有效地预测地理缩 (GA) 病变大小和与年龄相关的黄斑变性 (AMD) 的增长率. 圆形区域和视网膜色素上皮层是准确预测的关键.

更多相关视频

Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

21.5K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.6K

相关实验视频

Last Updated: Jul 19, 2026

Thinned-skull Cortical Window Technique for In Vivo Optical Coherence Tomography Imaging
07:28

Thinned-skull Cortical Window Technique for In Vivo Optical Coherence Tomography Imaging

Published on: November 19, 2012

15.1K
Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

21.5K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.6K

科学领域:

  • 眼科医生 眼科 眼科
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 与年龄相关的黄斑变性 (AMD) 是导致视力丧失的主要原因.
  • 地理缩 (GA) 是一种晚期的AMD,其特征是视网膜逐渐退化.
  • 准确预测GA进展对于监测疾病和评估治疗非常重要.

研究的目的:

  • 评估不同处理方法对3D光学连贯断层扫描 (OCT) 图像的性能.
  • 确定这些方法在与深度学习模型一起使用时的有效性,以预测GA损伤面积和生长速度.
  • 使用OCT数据确定最佳的图像处理策略,用于GA进展预测.

主要方法:

  • 从lampalizumab临床试验中使用的OCT体积,包括1219只眼睛用于开发和442只眼睛用于评估.
  • 评估了四种OCT图像处理方法:面对面强度图,SLIVER-net,3D卷积神经网络 (CNN) 和细分衍生图.
  • 采用CNN模型预测基线GA病变大小和年增长率,使用经过处理的OCT数据.

主要成果:

  • 所有评估的方法在预测GA增长率 (r2 ≈ 0.33 0.35) 中表现相似.
  • 基线GA损伤大小预测也相当 (r2 ≈ 0.90.91),而SLIVER-net的表现略低 (r2 = 0.83).
  • 圆形区域 (EZ) 和视网膜色素上皮层 (RPE) 层的厚度图是最有信息的;将它们结合起来改善了预测,而添加其他层却没有.

结论:

  • 当前的处理方法在预测GA增长率方面实现了可比的,可能停滞不前的性能.
  • EZ和RPE视网膜层含有预测GA进展的最重要的信息.
  • 3D OCT成像对于预测GA疾病进展具有相当大的实用性.