MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling

Robust segmentation is critical for deriving quantitative measures from large-scale, multi-center, and longitudinal medical scans. Manually annotating medical scans, however, is expensive and labor-intensive and may not always be available in every domain. Unsupervised domain adaptation (UDA) is a w...

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Vydáno v:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) Ročník 2024; s. 5851 - 5862
Hlavní autoři: Zhang, Xuzhe, Wu, Yuhao, Angelini, Elsa, Li, Ang, Guo, Jia, Rasmussen, Jerod M., OConnor, Thomas G., Wadhwa, Pathik D., Jackowski, Andrea Parolin, Li, Hai, Posner, Jonathan, Laine, Andrew F., Wang, Yun
Médium: Konferenční příspěvek Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.06.2024
Témata:
Computational modeling
Computer vision
generalization
Image segmentation
Magnetic resonance imaging
masked image modeling
medical image
Pediatrics
Reviews
self-supervised learning
Three-dimensional displays
unsupervised domain adaptation
ISSN:1063-6919, 1063-6919
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Shrnutí:Robust segmentation is critical for deriving quantitative measures from large-scale, multi-center, and longitudinal medical scans. Manually annotating medical scans, however, is expensive and labor-intensive and may not always be available in every domain. Unsupervised domain adaptation (UDA) is a well-studied technique that alleviates this label-scarcity problem by leveraging available labels from another domain. In this study, we introduce Masked Autoencoding and Pseudo-Labeling Segmentation (MAPSeg), a unified UDA framework with great versatility and superior performance for heterogeneous and volumetric medical image segmentation. To the best of our knowledge, this is the first study that systematically reviews and develops a framework to tackle four different domain shifts in medical image segmentation. More importantly, MAPSeg is the first framework that can be applied to centralized, federated, and test-time UDA while maintaining comparable performance. We compare MAPSeg with previous state-of-the-art methods on a private infant brain MRI dataset and a public cardiac CT-MRI dataset, and MAPSeg outperforms others by a large margin (10.5 Dice improvement on the private MRI dataset and 5.7 on the public CT-MRI dataset). MAPSeg poses great practical value and can be applied to real-world problems. GitHub: https://github.com/XuzheZ/MAPSeg/.
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Co-senior supervising authors.
Co-first authors.
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR52733.2024.00559