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相关概念视频

Computed Tomography01:10

Computed Tomography

4.3K
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...
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Appendicitis-II: Diagnostic Studies and Management01:29

Appendicitis-II: Diagnostic Studies and Management

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Diagnosing and managing appendicitis requires a structured and comprehensive approach that spans from initial assessment to postoperative care. Here is an overview of the process:
Diagnosing Appendicitis
It requires a multifaceted approach, starting with a detailed physical examination to pinpoint the location and nature of the pain and identify any associated symptoms. Laboratory tests play a crucial role. A complete Blood Count (CBC) typically reveals leukocytosis (an increased number of...
66
Ultrasound II: Endoscopic Ultrasound and FibroScan01:25

Ultrasound II: Endoscopic Ultrasound and FibroScan

82
Endoscopic Ultrasound (EUS) and FibroScan are valuable diagnostic tools in gastroenterology and hepatology, each with specific applications and techniques.
Endoscopic Ultrasound (EUS):
82
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

3
Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
3
Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy01:26

Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy

56
This lesson explores three gastrointestinal imaging techniques: radionuclide testing, colonic transit studies, and virtual colonoscopy.
Radionuclide Testing
Radionuclide testing is a sophisticated medical technique for assessing gastrointestinal motility. It focuses on gastric emptying and colonic transit time. Radioactive markers track the movement of food through the digestive system, providing insights into gastrointestinal disorders.
In gastric emptying studies, a meal's liquid and...
56
Appendicitis-I: Introduction01:22

Appendicitis-I: Introduction

91
The appendix, a small, narrow, blind tube extending from the inferior part of the cecum, is widely regarded as a vestigial organ, having lost much of its original function through evolution. Despite its diminished role, the appendix can become inflamed, a condition known as appendicitis.
Etiology: Appendicitis can arise from various causes, primarily rooted in the obstruction of the appendix lumen. Factors contributing to this obstruction include fecal accumulation, lymphoid hyperplasia and, in...
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相关实验视频

Updated: Jun 10, 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

Published on: November 30, 2022

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使用U-Net深度学习架构在CT扫描中完全自动检测附件.

Betül Tiryaki Baştuğ1, Gürkan Güneri2, Mehmet Süleyman Yıldırım3

  • 1Department of Radiology, Medical Faculty, Bilecik Şeyh Edebali University, Bilecik 11230, Türkiye.

Journal of clinical medicine
|October 16, 2024
PubMed
概括

这项研究引入了U-Net深度学习模型,用于CT扫描中的自动化尾细分,提高尾炎等疾病的诊断准确性.

关键词:
这是一个U-Net架构.附录检测检测 附录检测检测深度学习是一种深度学习.医学成像医学成像细分化 细分化的细分化

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Author Spotlight: Using Point-of-Care Ultrasound for Comprehensive Evaluation of the Abdominal Aorta
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Author Spotlight: Using Point-of-Care Ultrasound for Comprehensive Evaluation of the Abdominal Aorta

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相关实验视频

Last Updated: Jun 10, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Author Spotlight: Using Point-of-Care Ultrasound for Comprehensive Evaluation of the Abdominal Aorta
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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
  • 放射学 放射学是一门学科.

背景情况:

  • 准确的尾细分对于诊断尾炎至关重要.
  • 手动的尾识别是耗时的,并且依赖于放射科医生的专业知识.
  • 需要自动化方法来提高效率和准确性.

研究的目的:

  • 开发一种全自动化的深度学习方法,用于CT扫描中检测尾.
  • 使用U-Net架构进行高效和高性能的附件细分.
  • 评估拟议模型的诊断可靠性.

主要方法:

  • 一个U-Net深度学习架构被用于附件细分.
  • 该模型是在腹部CT扫描的注释数据集上进行训练的.
  • 应用了数据增强技术来扩展训练数据集.
  • 使用超参数优化来完善模型的性能.

主要成果:

  • U-Net 模型实现了高细分性能,子相似系数 (DSC) 为 85.94%.
  • 关键指标包括23.29%的体积重叠误差 (VOE) 和1.24毫米的平均对称表面距离 (ASSD).
  • 与其他方法相比,该模型表现出优越的性能,利用U-Net的上下文理解.

结论:

  • 拟议的U-Net模型在CT扫描中提供可靠的尾细分.
  • 深度学习显示了改善尾检测临床结果的巨大潜力.
  • 局限性包括当附录靠近其他结构时的细分挑战.