跨视图差异依赖网络用于体积医学图像细分的跨视图差异依赖网络
Shengzhou Zhong1, Wenxu Wang1, Qianjin Feng1
1School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, Guangdong, 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China.
医学成像中的有限数据阻碍了深度学习细分. 我们的交叉视图差异依赖网络 (CvDd-Net) 有效地使用多视图切片,解决视图差异和依赖关系,以提高体积细分精度.
科学领域:
- 医疗成像医学成像
- 深度学习 (Deep Learning) 是一种深度学习.
- 计算机视觉 计算机视觉
背景情况:
- 有限的数据是基于深度学习的体积医学图像细分的主要挑战.
- 现有的多视图切片方法往往忽视了切片间的空间连续性和交叉视图关系.
研究的目的:
- 提出一个新的网络,交叉视图差异依赖网络 (CvDd-Net),用于体积医学图像细分.
- 通过利用多视图切片先验来增强体积表示学习,特别是解决视图差异和依赖性.
主要方法:
- 开发了一个异常意识形态增强 (DaMR) 模块,以学习视图特定的表示,使用形态信息 (对象边界和位置).
- 设计了一个依赖意识的信息聚合 (DaIA) 模块,以集成基于交叉视图依赖的多视图切片信息.
- 在使用四个医学图像数据集的完全监督和半监督任务上评估了该方法:甲状腺,宫,胰腺和质瘤.
主要成果:
- 拟议的CvDd-Net有效地提高了体积医学图像细分性能.
- 通过解决差异和依赖关系,DaMR和DaIA模块成功地利用多视图信息.
- 在各种医学成像数据集和细分任务中表现出显著的有效性.
结论:
- CvDd-Net为体积医疗图像细分提供了一个强大的解决方案,特别是在数据有限的场景中.
- 利用交叉视图差异和依赖性对于推进医学成像中的多视图表示学习至关重要.
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