Unsupervised domain adaptation approaches have recently succeeded in various medical image segmentation tasks. The reported works often tackle the domain shift problem by aligning the domain-invariant features and minimizing the domain-specific discrepancies. That strategy works well when the difference between a specific domain and between different domains is slight. However, the generalization ability of these models on diverse imaging modalities remains a significant challenge. This paper introduces UDA-VAE++, an unsupervised domain adaptation framework for cardiac segmentation with a compact loss function lower bound. To estimate this new lower bound, we develop a novel Structure Mutual Information Estimation (SMIE) block with a global estimator, a local estimator, and a prior information matching estimator to maximize the mutual information between the reconstruction and segmentation tasks. Specifically, we design a novel sequential reparameterization scheme that enables information flow and variance correction from the low-resolution latent space to the high-resolution latent space. Comprehensive experiments on benchmark cardiac segmentation datasets demonstrate that our model outperforms previous state-of-the-art qualitatively and quantitatively. The code is available at https://github.com/LOUEY233/Toward-Mutual-Information}{https://github.com/LOUEY233/Toward-Mutual-Information

本文介绍了一种名为UDA-VAE ++的无监督域适应框架，该框架利用互信息的估计值和全局估计器、局部估计器以及先验信息匹配估计器之间的结构互信息估计（SMIE）块，提出了一种新的下限来解决心脏分割问题，并通过一种创新的顺序重新参数化方案来达到更高的性能，该方案能够从低分辨率的潜空间到高分辨率的潜空间实现信息流和方差校正。实验表明，我们的模型在基准心脏分割数据集上表现优异。

基于结构互信息最大化的无监督心脏分割域适应