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Imaging Studies I: Kidney, Ureter, and Bladder Studies01:28

Imaging Studies I: Kidney, Ureter, and Bladder Studies

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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风格转移辅助的深度学习方法用于多相MRI中的脏细分.

Junyu Guo1, Manu Goyal1, Yin Xi1

  • 1From the Department of Radiology (J.G., M.G., Y.X., L.H., G.H., E.A., I.P.), Department of Urology (I.P.), and Advanced Imaging Research Center (I.P.), University of Texas Southwestern Medical Center, 2201 Inwood Rd, Suite 202, Dallas, TX 75390-9085.

Radiology. Artificial intelligence
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概括

一种新的深度学习方法在多相对照增强型MRI扫描中准确地细分脏. 这种方法利用生成对抗网络 (GAN) 和卷积神经网络 (CNN) 进行精确的细分.

关键词:
卷积神经网络是一个卷积神经网络.循环GANAN是一个循环.生成性的对抗网络.细分的细分 细分的细分转移学习学习 转移学习

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科学领域:

  • 医疗成像医学成像
  • 放射学中的人工智能
  • 用于医学图像分析的深度学习

背景情况:

  • 准确的细分对于诊断和监测病至关重要.
  • 在多相对比增强 (MCE) MRI 中手动细分脏是耗时的,并且受观察者之间的变化影响.
  • 开发自动化细分方法可以提高临床实践的效率和一致性.

研究的目的:

  • 开发和验证一个半监督的,样式转移辅助的深度学习方法,用于自动化脏细分.
  • 为了利用多相对比增强 (MCE) 的MRI获取来进行强大的脏细分.
  • 评估拟议的深度学习模型的性能与手动细分相比.

主要方法:

  • 训练了一个循环一致的生成对抗网络 (CycleGAN),从T2加权图像中生成类似MCEMRI的合成数据集.
  • 基于面膜区域的卷积神经网络 (CNN) 在这些合成数据集上进行了训练,用于细分.
  • 该模型在125名患者中进行了训练,并在20个MCEMRI检查的单独队列中得到了验证.
  • 用Dice和Jaccard分数来评估细分的性能.

主要成果:

  • 在CycleGAN成功生成了解剖学上共同注册的合成MCEMRI数据集.
  • 深度学习方法在所有四个MCEMRI阶段都取得了高的平均Dice分数:0.91 (前对照),0.92 (皮膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜膜).
  • 该方法在多种MCEMRI采集中在脏细分方面表现出高性能.

结论:

  • 拟议的半监督深度学习方法,利用CycleGAN和CNNs的样式传输,提供准确和自动化的脏细分.
  • 这种方法显示出提高脏MRI分析效率和一致性的巨大潜力.
  • 该技术在MCE MRI的各个阶段都有效,为临床应用提供了一种多功能工具.