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

• URL: http://arxiv.org/abs/2204.09334v3
• Date: Sat, 27 May 2023 15:42:45 GMT
• Title: Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization
• Authors: Changjie Lu, Shen Zheng, Gaurav Gupta
• Abstract summary: Unsupervised domain adaptation approaches have succeeded in various medical image segmentation tasks. UDA-VAE++ is an unsupervised domain adaptation framework for cardiac segmentation with a compact loss function lower bound. Our model outperforms previous state-of-the-art qualitatively and quantitatively.
• Score: 0.8959391124399926
• License: http://creativecommons.org/licenses/by/4.0/
• 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

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