Abstract
In this paper, we propose an unsupervised framework based on normalizing flows that harmonizes MR images to mimic the distribution of the source domain. The proposed framework consists of three steps. First, a shallow harmonizer network is trained to recover images of the source domain from their augmented versions. A normalizing flow network is then trained to learn the distribution of the source domain. Finally, at test time, a harmonizer network is modified so that the output images match the source domain's distribution learned by the normalizing flow model. Our unsupervised, source-free and task-independent approach is evaluated on cross-domain brain MRI segmentation using data from four different sites. Results demonstrate its superior performance compared to existing methods.
Abstract (translated)
在本文中,我们提出了一种基于正则化流量的无监督框架,该框架旨在协调MR图像,以模拟源领域的分布。该框架由三个步骤组成。首先,一个浅度协调器网络被训练以从增强的版本中恢复源领域的图像。然后,一个正则化流网络被训练以学习源领域的分布。最后,在测试时,协调器网络被修改,以使其输出图像与正则化流模型学习到的源领域的分布匹配。我们的无源、无任务独立的方法在跨域脑MRI分割任务中进行了评估,使用来自四个不同站点的数据。结果表明,它与现有方法相比表现出更好的性能。
URL
https://arxiv.org/abs/2301.11551
