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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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自动双心房细分和生物标志物提取从晚期加多增强型MRI使用深度学习.

Fan Feng1, James Kennelly1, Zhaohan Xiong1

  • 1Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.

Expert systems with applications
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概括
此摘要是机器生成的。

一个新的深度学习工具,biAtriaNet,准确地对两个心房进行细分,并从LGE-MRI中量化纤维化,心房壁厚度和腔体积. 这推动了个性化的心房移除策略的发展.

关键词:
这就是LGE-MRI.心房动是心房动的一种.心脏磁共振成像 - 心脏磁共振成像深度学习是一种深度学习.纤维化 纤维化晚期加多增强的MRI.医疗图像细分 医疗图像细分

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科学领域:

  • 心血管成像 - 心血管成像
  • 人工智能在医学中的应用
  • 生物医学工程 生物医学工程

背景情况:

  • 心房动 (AF) 涉及逐渐的心房重塑,包括扩张和纤维化,影响治疗疗效.
  • 晚期加多增强 (LGE) 核磁共振测量左心房纤维化 (LA),但缺乏对两大心房的强大细分和准确的生物标志物评估.
  • 目前的方法往往不包括右心房 (RA),并难以准确地进行解剖和纤维化表征.

研究的目的:

  • 引入biAtriaNet,这是一个深度学习管道,用于自动细分和从LA和RA的LGE-MRIs中提取生物标志物.
  • 评估心房纤维化,心房壁厚度 (AWT) 和腔室尺寸/体积,以改善AF切除指导.
  • 为患者特定的AF治疗策略开发一个强大的工具.

主要方法:

  • 开发了biAtriaNet,一个使用两个CNN的深度学习管道,采用修改的U-Net架构,剩余连接和批量规范化.
  • 在二维电影MRI (英国生物库,n=4860) 和三维LGE-MRI (犹他大学,n=60) 上接受过培训和验证.
  • 在11个3DLGE-MRI (怀卡托医院,新西兰) 上进行独立测试,与专家注释和地面真相进行比较.

主要成果:

  • biAtriaNet实现了高细分精度 (Dice分数:LA 91.1%,RA 88.6%) 和可转移到独立数据集.
  • 室内体积和AWT测量显示出高精度 (LA分别为90%和95.9%,RA分别为94.6%).
  • 纤维化估计显示出强烈的相关性 (科尔莫戈罗夫-斯米尔诺夫:LA 86.3%,RA 90.6%,p < 0.05).

结论:

  • 通过biAtriaNet,可以从LGE-MRIs中准确,自动化地进行双心房细分和生物标志物提取.
  • 该管道提供了对心房解剖学和纤维化的可靠量化,这对于AF管理至关重要.
  • 这种工具具有显著的潜力,可以增强针对患者的AF消去策略,并改善临床结果.