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相关概念视频

Computed Tomography01:10

Computed Tomography

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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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Imaging Studies III: Computed Tomography01:27

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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...
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相关实验视频

Updated: Sep 9, 2025

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
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通过计算机断层扫描自动检测死亡软组织感染特征

Heng-Yu Lin1, Ming-Chuan Chiu2, Tzu-Lun Kao2

  • 1Division of Plastic Surgery, Department of Surgery, Chi Mei Medical Center, Tainan 710, Taiwan.

Diagnostics (Basel, Switzerland)
|August 28, 2025
PubMed
概括

使用YOLOv10的自动化系统有效地检测CT扫描中的软组织感染 (NSTI) 特征. 这种人工智能工具有助于更快地诊断和改善NSTI的手术规划.

关键词:
YOLOv10 时间人工智能计算机断层扫描死亡软组织感染对象检测

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相关实验视频

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科学领域:

  • 医学成像
  • 人工智能
  • 放射学

背景情况:

  • 死亡软组织感染 (NSTI) 需要及时诊断才能有效治疗.
  • 计算机断层扫描 (CT) 对于识别NSTI特征至关重要.
  • 自动检测系统可以提高诊断效率.

研究的目的:

  • 开发和评估CT图像上的NSTI特征的自动检测系统.
  • 在此任务中使用"你只看一次"版本10 (YOLOv10) 模型.
  • 提高NSTI的诊断效率和手术规划.

主要方法:

  • 31名经过手术确认的NSTIs患者的回顾性研究 (2017-2023年).
  • 对于四个NSTI特征的9001CT图像的注释:子宫外气体,液体积累,膜胀和非增强.
  • 使用平均精度 (mAP),回忆和精度进行性能评估.

主要成果:

  • 在YOLOv10模型中,整体mAP为0.75.
  • 回忆和精度分别为0.74和0.72.
  • 粘膜 edematous 变化 (0. 92) 和软组织外宫气体 (0. 76) 的高回忆率.

结论:

  • 基于YOLOv10的系统在CT检测关键NSTI特征方面表现出有效性.
  • 这种人工智能方法有望改善NSTI诊断和手术规划.
  • 该系统准确地识别软组织外气体,液体积累,带变化和软组织不增强.