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没有对比的心肌梗塞细分与注意力U-Net.

Khaled Ali Deeb1, Yasmeen Alshelle2, Hala Hammoud2

  • 1Department of Information Processing and Management Systems, Bauman Moscow State Technical University, 105005 Moscow, Russia.

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概括
此摘要是机器生成的。

这项研究引入了一个深度学习框架,用于自动心脏细分和心肌梗塞检测,使用非对比心血管磁共振成像,提高效率和可访问性.

关键词:
在美国,CNN是CNN.在U-Net的注意力.心脏磁力共振成像 (MRI)电影电影 CMR CMR 的时间深度学习是一种深度学习.心肌梗塞的心脏病发作细分化 细分化的细分化

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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
  • 心脏病学 心脏病学

背景情况:

  • 心血管磁共振 (CMR) 是心脏评估的黄金标准,但手动细分是耗时和可变的.
  • 深度学习 (DL) 实现了细分的自动化,但在用于心肌梗塞 (MI) 检测的非对比电影CMR中却难以实现泛化.

研究的目的:

  • 开发一个DL框架用于自动心脏结构和MI细分,使用无对比的CineCMR.
  • 通过实现对比度独立的心脏评估来提高CMR的临床适用性.

主要方法:

  • 集成多个CNN用于心脏结构细分和基于注意力的DL模型用于MI定位.
  • 使用堆叠的自动编码器和积极的轮建模来实现解剖学一致性的后处理.
  • 使用子相似系数 (DSC),平均轮距离 (MCD) 和豪斯多夫距离 (HD) 评估性能.

主要成果:

  • 获得了高的子得分:0.93为LV腔,0.89为LV心肌,0.91为RV腔.
  • 证明了可靠的MI细分与0.80的子得分和高回忆.
  • 在所有细分心脏结构中展示了始终低的边界误差.

结论:

  • 拟议的DL框架允许准确,没有对比度的心脏结构和MI从cine CMR的细分.
  • 促进更广泛的临床使用,特别是对于具有对比性禁忌的患者或在资源有限的环境中.
  • 支持独立于对比剂的可扩展和可靠的心脏评估.