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相关概念视频

Computed Tomography01:10

Computed Tomography

4.5K
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...
4.5K
Ultrasound I: Abdominal Ultrasonography01:20

Ultrasound I: Abdominal Ultrasonography

230
Introduction:
Abdominal ultrasonography, commonly known as abdominal ultrasound, is a vital, non-invasive medical imaging technique widely used in healthcare.
Procedure:
This diagnostic tool allows the clinician to visually inspect internal structures within the abdomen, including vital organs such as the liver, gallbladder, pancreas, kidneys, and spleen.
The abdominal ultrasound process begins with applying a special gel to the patient's skin over the abdomen. This gel enhances the...
230
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

245
Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
245

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相关实验视频

Updated: Jul 5, 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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基于互动内容的图像检索与深度学习用于CT腹部器官识别.

Chung-Ming Lo1, Chi-Cheng Wang2, Peng-Hsiang Hung2

  • 1Graduate Institute of Library, Information and Archival Studies, National Chengchi University, Taipei, Taiwan.

Physics in medicine and biology
|January 17, 2024
PubMed
概括

这项研究开发了一个基于内容的图像检索 (CBIR) 系统,使用深度学习在CT扫描中自动识别七个腹部器官. 该系统实现了高精度,提供了有价值的临床决策支持.

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 放射学 放射学是指放射学

背景情况:

  • 在计算机断层扫描 (CT) 中精确识别腹部器官对于临床诊断和治疗计划至关重要.
  • 手动器官识别可能耗时,需要专门的专业知识.
  • 开发用于器官识别的自动化系统可以提高放射性评估的效率和一致性.

研究的目的:

  • 提出和评估基于内容的自动图像检索 (CBIR) 系统,用于识别CT片中的七个关键腹部器官.
  • 利用深度学习架构来进行特征提取和器官分类.
  • 评估系统在为临床使用提供类似证据方面的表现.

主要方法:

  • 一个数据集包括2827个腹部CT切片,包括肝脏,胃,胰腺,脏,右脏,左脏和胆囊.
  • 包括DenseNet,Vision Transformer (ViT) 和Swin Transformer v2 (SwinViT) 在内的深度学习模型被微调为特征提取.
  • 使用分类准确性和检索性能指标评估了CBIR系统.

主要成果:

  • 该系统实现了高分类准确度,从94%到99%不等,检索结果在0.98到0.99.
  • 在考虑全球功能和多个分辨率方面,SwinViT的表现优于ViT的表现,而ViT的表现优于DenseNet的表现,因为它具有更好的接收场.
关键词:
腹部CTCT可以使用.基于内容的图像检索.深度学习是一种深度学习.视觉变压器 视觉变压器

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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  • 使用"洞图像"显著提高了性能,在所有测试的深度学习架构中产生了近乎完美的结果.
  • 结论:

    • 预先训练有素的深度学习模型,当与足够的数据微调时,可以在CT图像中有效地识别七个腹部器官.
    • 拟议的CBIR系统提供了一种强大的方法,可以通过相似性测量来识别腹部器官,从而有可能增强临床实践.
    • 这种自动化方法为器官识别提供了更有说服力的证据,为临床应用开辟了新的途径.