Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization
Document Type
Conference Proceeding
Publication Date
1-1-2022
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.
Publication Title
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
First Page Number
2587
Last Page Number
2596
DOI
10.1109/CVPRW56347.2022.00291
Recommended Citation
Lu, Changjie; Zheng, Shen; and Gupta, Gaurav, "Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization" (2022). Kean Publications. 754.
https://digitalcommons.kean.edu/keanpublications/754
