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基于挤压激发的双分支编码器网络和用于3D PET-CT图像瘤细分的变压器

Mingrui Li¹, Ruiming Zhu¹, Minghao Li¹

¹College of Medicine and Biomedical Information Engineering, Northeastern University, 110169, Shenyang, China.

Medical & biological engineering & computing

|September 5, 2025

概括

这项研究介绍了使用PET和CT数据进行自动瘤细分的先进深度学习模型TASE-UNet. 它实现了卓越的准确性,超过了HECKTOR2022数据集中的现有方法.

科学领域:

医学成像
人工智能
辐射学

背景情况:

手动细分瘤需要大量的时间和专业知识.
准确的瘤细分对于临床实践和放射学分析至关重要.
深度学习为自动化瘤细分提供了一个有前途的方法.

研究的目的:

通过整合PET和CT数据开发一个准确和自动化的瘤细分模型.
通过利用新的双分支编码器架构和注意力机制来提高细分精度.
在具有挑战性的基准数据集上验证提议的TASE-UNet模型.

主要方法:

一个基于SE-UNet和Transformer的新型双分支编码器架构被设计出来.
一个3D卷积区注意模块 (CBAM) 被纳入跳过连接.
在训练阶段使用了二进制交叉 (BCE) 损失.
这个名为TASE-UNet的模型在HECKTOR2022数据集上进行了评估.

主要成果:

在HECKTOR2022数据集上,TASE-UNet实现了最先进的细分精度.
该模型获得了76.10%的子相似系数 (DSC) 和3.27的豪斯多夫距离95.
比较分析显示,比现有最先进的方法更高的性能.

结论:

开发的TASE-UNet模型对于自动化瘤细分是有效和可靠的.
整合多模式成像数据 (PET和CT) 提高了细分性能.
建议的架构和注意力机制有助于提高细分精度.

关键词:

3D医学图像细分注意机制美国有线电视多模式的PET-CT 变压器

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