Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization

TL;DR

This paper presents UDA-VAE++, an unsupervised domain adaptation framework for cardiac segmentation that maximizes structure mutual information to improve generalization across diverse imaging modalities, outperforming previous methods.

Contribution

The paper introduces a novel Structure Mutual Information Estimation (SMIE) block and a sequential reparameterization scheme for better domain adaptation in cardiac segmentation.

Findings

Outperforms state-of-the-art methods on benchmark datasets.

Effectively maximizes mutual information between reconstruction and segmentation.

Demonstrates improved generalization across diverse imaging modalities.

Abstract

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