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DAWTran:用于肺胸部细分的动态自适应式窗口变压器网络,含有隐式特征对齐.

Pengchen Liang1, Jianguo Chen2, Lei Yao1

  • 1The Department of School of Microelectronics, Shanghai University, Shanghai, 201800, People's Republic of China.

Physics in medicine and biology
|August 4, 2023
PubMed
概括

本研究介绍了动态自适应窗口变压器 (DAWTran),用于改善CT扫描中的肺胸部细分. 新型网络提高了准确性,减少了错误,为临床诊断提供了有价值的工具.

关键词:
动态自适应式窗户设计隐性特征对齐 隐性特征对齐医疗图像分析分析肺胸部细分的细分方式变压器的变压器是一个变压器.

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科学领域:

  • 医疗成像医学成像
  • 人工智能在医学中的应用
  • 计算机辅助诊断 计算机辅助诊断

背景情况:

  • 在CT图像中肺胸部细分具有挑战性,因为肺胸部和气体充满结构的外观相似.
  • 准确的细分对于及时诊断和治疗肺胸病至关重要.

研究的目的:

  • 在计算机断层扫描 (CT) 图像中开发一种用于精确细分肺胸部的新型网络.
  • 为了克服由肺胸和气管和支气管等解剖结构之间的视觉相似性引起的细分困难.

主要方法:

  • 引入具有编码器-解码器架构的动态自适应式窗口变压器 (DAWTran) 网络.
  • 实施动态自适应窗口策略,通过多头自我注意来进行多规模的特征提取.
  • 在解码器中利用隐性特征对齐来最大限度地减少信息偏差.
  • 应用混合损失函数来解决类不平衡.

主要成果:

  • DAWTran实现了91.35%的子相似系数 (DSC),表现比TransUNet高2.21%.
  • 该网络显著减少了豪斯多夫距离 (HD) 到8.06毫米,比TransUNet.net提高了29.92%.
  • 与SwinUnet.net相比,动态自适应窗口 (DAW) 机制增加了4.53%的DSC,并减少了15.85%的HD.
  • 隐式特征对齐 (IFA) 进一步提高了准确性,增加了0.11%的DSC,减少了10.01%的HD.

结论:

  • 在临床环境中,DAWTran网络显示了用于精确的肺胸部细分的巨大潜力.
  • 拟议的DAW和IFA组件有效地提高了细分性能.
  • 这种方法为提高肺胸病诊断和治疗规划的准确性提供了一个有前途的工具.