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相关概念视频

Computed Tomography01:10

Computed Tomography

4.6K
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...
4.6K
Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

4.8K
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...
4.8K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

28
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...
28

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相关实验视频

Updated: Jul 21, 2025

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

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标注 有效学习用于OCT细分.

Haoran Zhang1, Jianlong Yang1, Ce Zheng2

  • 1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Biomedical optics express
|July 27, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一种高效的深度学习方法,用于光学一致性断层扫描 (OCT) 细分,显著减少数据注释需求. 该方法以更少的数据和更快的培训时间实现了可比的准确性,增强了OCT技术应用.

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Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo
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Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo

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Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography
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Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Published on: March 26, 2020

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相关实验视频

Last Updated: Jul 21, 2025

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

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Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo
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Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo

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Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography
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Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 对于OCT细分的深度学习模型需要大量的数据注释.
  • 标注是耗时和昂贵的,限制了OCT在诸如外科导航和多中心试验等领域的应用.

研究的目的:

  • 开发一个注释效率高的学习方法,用于OCT细分.
  • 为了减少与OCT细分模型数据注释相关的成本和时间.

主要方法:

  • 利用基于变压器的模型用于OCT图像的自我监督生成学习.
  • 将变压器编码器与CNN解码器集成在一起,用于密集的像素智能预测.
  • 引入了用于选择性数据注释的k中心算法.

主要成果:

  • 仅使用一小部分培训数据,实现了与U-Net相似的细分精度.
  • 演示了高达3.5倍更快的训练时间.
  • 优于其他注释效率策略.

结论:

  • 拟议的方法大大降低了标注费用和用于OCT细分的培训时间.
  • 预训练模型适应各种数据集和ROI,无需重新训练.
  • 这种学习效率可以提高OCT技术的智能化和采用.