一个高效,可扩展和可适应的插即用时间注意模块,用于以运动为导向的心脏细分,具有稀疏的时间标签
Md Kamrul Hasan1, Guang Yang2, Choon Hwai Yap1
1Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK.
Medical image analysis
|February 15, 2026
概括
本研究引入了一个时间注意模块 (TAM),以改善深度学习 (DL) 模型中的心脏解剖细分. 在各种心脏成像数据集和网络中,TAM提高了运动感知细分效率和准确性.
科学领域:
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
- 心血管成像 - 心血管成像
背景情况:
- 心脏解剖学细分对于评估心脏功能和诊断疾病至关重要.
- 深度学习 (DL) 模型已经提高了细分的准确性,特别是在结合心脏运动动态时.
- 现有的集成运动信息的方法在计算上昂贵,或者容易出现注册错误.
研究的目的:
- 开发一个计算效率高和强大的时间注意力模块 (TAM),以增强基于深度学习的心脏解剖细分.
- 创建一个可适应各种细分网络架构 (CNN,变压器,混合) 的插入运行模块.
- 为了提高心脏运动感知细分的准确性和效率.
主要方法:
- 提出了一个新的时间注意力模块 (TAM),使用一个多头,跨时间的注意力机制.
- 将TAM集成到各种细分网络中,包括UNet,FCN8s,UNetR,SwinUNetR,I2UNet和DT-VNet.
- 在多个心脏成像数据集上验证了TAM:二维回声心脏图像 (CAMUS,EchoNet-Dynamic),三维回声心脏图像 (MITEA) 和三维心脏MRI (ACDC).
主要成果:
- 在所有测试的数据集和网络中,TAM 始终改善了细分性能.
- 将TAM集成到SAM和MedSAM中显著减少了豪斯多夫距离 (HD).
- 在ACDC的3D数据集中,TAM-UNet和TAM-DT-VNet证明了大量的HD减少.
- TAM培训只需要稀疏的时间注释,而不是所有的完整地面真实细分.
结论:
- 时间注意模块 (TAM) 为运动感知心脏图像细分提供了一个强大的,可泛化和可适应的解决方案.
- TAM可以提高细分精度和效率,而无需大幅修改架构,从2D扩展到3D扩展.
- 拟议的方法减少了计算成本和对复杂的注册技术的依赖,使其成为临床应用的实际进步.
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