斯特鲁网:用于医学图像染的感知和低级调节变压器
Yuhui Ma1,2, Qifeng Yan1, Yonghuai Liu3
1Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences, Cixi, China.
Medical physics
|June 6, 2023
概括
一个新的深度学习模型StruNet有效地从CT和OCT扫描等医疗图像中去除各种噪音工件. 这种统一的方法可以提高图像质量,而不会丢失关键细节,从而提高诊断准确度.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 计算机断层扫描 (CT),光学连贯断层扫描 (OCT) 和光学连贯断层扫描血管扫描 (OCTA) 等医疗成像模式中的噪音人工物可以阻碍诊断和分析.
- 当前的深度学习方法难以灵活地去除各种噪音工件,同时在不同的模式中保留必要的图像细节.
研究的目的:
- 引入一种基于Swin变压器的新型残余U形网络 (StruNet),用于统一的医疗图像消毒.
- 解决现有方法在处理各种噪声分布和保持图像保真方面的局限性.
主要方法:
- 拟议的StruNet采用编码器-解码器架构,其核心块并行集成Swin变压器模块和剩余块.
- 斯温变压器模块通过自我注意和窗口连接捕获层次噪声表示.
- 带有快捷连接的剩余块保留了详细的图像信息,而感知损失和低级规则化确保了特征一致性和低级特征.
主要成果:
- 对计算机断层扫描 (CT),光学连贯性断层扫描 (OCT) 和光学连贯性断层扫描血管学 (OCTA) 数据集进行了实验.
- 在这些不同的医疗成像模式中,StruNet架构证明了有效地抑制各种噪音工件.
结论:
- 拟议的StruNet架构显示出在各种医学成像模式中消除多种形式的噪音工件的重大前景.
- 这种统一的方法为改善医疗图像质量和诊断可靠性提供了潜在的解决方案.
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