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相关概念视频

Imaging Studies VI: Voiding Cystourethrography and Cystography01:22

Imaging Studies VI: Voiding Cystourethrography and Cystography

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Voiding Cystourethrography (VCUG) and Cystography are specialized radiographic procedures used to examine the structure and function of the bladder and urethra.Voiding Cystourethrography (VCUG)A Voiding Cystourethrogram (VCUG) is a diagnostic imaging procedure that assesses the anatomy and function of the lower urinary tract. It focuses on the bladder, bladder neck, and urethra, helping detect abnormalities such as vesicoureteral reflux (VUR)—the backward or reverse flow of urine into the...
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Imaging Studies I: Kidney, Ureter, and Bladder Studies01:28

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Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...
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Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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相关实验视频

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Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors
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基于MRI的膀癌分期通过YOLOv11细分和深度学习分类.

Phisit Katongtung1, Kanokwatt Shiangjen1, Watcharaporn Cholamjiak2

  • 1School of Information and Communication Technology, University of Phayao, Phayao 56000, Thailand.

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概括

这项研究引入了一种自动化的深度学习框架,用于使用MRI扫描进行膀癌分期. 人工智能模型在区分非肌肉侵入性和肌肉侵入性疾病方面表现出高准确性,支持标准化放射学解释.

关键词:
这就是为什么MRI是MRI.膀癌的分期 膀癌的分期深度学习是一种深度学习.混合细分化混合细分化支持以放射学为导向的工作流程.

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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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科学领域:

  • 放射学 放射学是一门学科.
  • 人工智能的人工智能
  • 在瘤学瘤学.

背景情况:

  • 精确的膀癌分期对于治疗决策至关重要,特别是区分非肌肉侵入性 (T1) 与肌肉侵入性 (T2-T4) 疾病.
  • 磁共振成像 (MRI) 提供了优越的软组织对比度,但受操作者依赖的解释和观察者之间的变化性限制.

研究的目的:

  • 开发和评估用于基于MRI的标准化膀癌分期的自动化深度学习框架.
  • 支持可重现的放射学解释和改善临床管理决策.

主要方法:

  • 开发了一个连续的AI管道,集成YOLOv11用于瘤细分和DeepLabV3用于边界精细化.
  • 三个深度学习分类器 (VGG19,ResNet50,Vision Transformer) 在416张T2权重的MRI图像上进行了训练,以进行阶段预测.
  • 使用准确度,精度,回忆,F1得分和多类AUC来评估性能,不确定性以引导置信区间为特征.

主要成果:

  • 所有评估的模型都表现出基于MRI的膀癌分期的高和可比的区分性能.
  • 获得了高精度和AUC,特别是在区分非肌肉侵入性和肌肉侵入性膀癌方面.
  • 校准分析证实了预测阶段概率的概率行为.

结论:

  • 拟议的深度学习框架证明了基于MRI的自动膀癌分期的可行性.
  • 该框架支持人工智能在标准化和复制基于MRI的分期程序方面的潜力,作为一致性的方法论工具.
  • 需要进一步验证多中心数据集,病理确认和可解释的AI,以获得概括性和临床相关性.