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流行性肺炎-UNet:细粒度特征挤压和多级级别布扩展语义聚合双解码器UNet用于COVID-19肺部感染细分从CT图像的CT图像

Wenfeng Wang¹, Qi Mao¹, Yi Tian¹

¹School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, People's Republic of China.

Biomedical physics & engineering express

|August 14, 2024

概括

一个新的FMD-UNet模型使用双解码器改进了COVID-19感染细分. 这种方法增强了语义信息的利用,并减少了模型参数,以实现更有效的计算机辅助诊断.

科学领域:

医疗成像医学成像
人工智能的人工智能
计算机辅助诊断计算机辅助诊断

背景情况:

自动细分COVID-19感染区域对于及时诊断和患者康复至关重要.
现有的基于U-Net的方法与细粒度的语义信息和编码器-解码器语义差距作斗争.

研究的目的:

提出一种新的双解码U-Net网络,FMD-UNet,以改善COVID-19感染细分.
提高精细的语义信息的利用率,并弥合医学图像细分中的语义差距.

主要方法:

开发了FMD-UNet集成一个细粒度特征挤压 (FGFS) 解码器和一个多尺度扩展语义聚合 (MDSA) 解码器.
FGFS解码器使用特征压缩和注意力来进行详细的语义捕获.
MDSA解码器使用具有扩展卷积的层次模块来融合多尺度特征并弥合语义差距.

主要成果:

在COVID-19细分的三个公共数据集上获得了84.76%,78.56%和61.99%的领先的子相似系数 (DSC) 得分.
与以前的方法相比,显示了较少的模型参数和较短的推断时间.
通过全面的评估和废弃性研究验证了可靠性和通用性.

结论:

通过利用双解码器架构,FMD-UNet有效地对COVID-19感染区域进行细分.
拟议的网络为计算机辅助诊断提供了具有竞争力和高效的解决方案,其性能优于现有的方法.
FMD-UNet显示了在COVID-19诊断和管理中临床应用的巨大潜力.

关键词:

在 COVID-19 疫情中,这就是U-Net.计算机辅助诊断是指计算机辅助的诊断.深度学习是一种深度学习.图像分割图像细分图像细分

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