Abstract
Unsupervised domain adaptation is very useful for medical image segmentation. Previous works mainly considered the situation with one source domain and one target domain. However in practice, multi-source and/or multi-target domains are generally available. Instead of implementing adaptation one by one, in this work we study how to achieve multiple domain alignment simultaneously to improve the segmentation performance of domain adaptation. We use the VAE framework to transform all domains into a common feature space, and estimate their corresponding distributions. By mixing domains and minimizing the distribution distance, the proposed framework extracts domain-invariant features. We verified the method on multi-sequence cardiac MR images for unsupervised segmentation. Results experimentally demonstrated that mixing target domains together could improve the segmentation accuracy, when the label distributions of mixed target domains are closer to that of the source domain than each unmixed target domain. Compared to state-of-the-art methods, the proposed framework obtained promising results.
This work was funded by the National Natural Science Foundation of China (grant no. 61971142, 62111530195 and 62011540404).
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Wu, F., Li, L., Zhuang, X. (2022). Multi-modality Cardiac Segmentation via Mixing Domains for Unsupervised Adaptation. In: Puyol Antón, E., et al. Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge. STACOM 2021. Lecture Notes in Computer Science(), vol 13131. Springer, Cham. https://doi.org/10.1007/978-3-030-93722-5_20
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DOI: https://doi.org/10.1007/978-3-030-93722-5_20
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