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Plug-and-Play Shape Refinement Framework for Multi-site and Lifespan Brain Skull Stripping

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conference contribution
posted on 2023-03-08, 10:14 authored by Y Li, R Dan, S Wang, Y Cao, X Luo, C Tan, G Jia, H Zhou, Y Zhang, Y Wang, L Wang

Skull stripping is a crucial prerequisite step in the analysis of brain magnetic resonance images (MRI). Although many excellent works or tools have been proposed, they suffer from low generalization capability. For instance, the model trained on a dataset with specific imaging parameters cannot be well applied to other datasets with different imaging parameters. Especially, for the lifespan datasets, the model trained on an adult dataset is not applicable to an infant dataset due to the large domain difference. To address this issue, numerous methods have been proposed, where domain adaptation based on feature alignment is the most common. Unfortunately, this method has some inherent shortcomings, which need to be retrained for each new domain and requires concurrent access to the input images of both domains. In this paper, we design a plug-and-play shape refinement (PSR) framework for multi-site and lifespan skull stripping. To deal with the domain shift between multi-site lifespan datasets, we take advantage of the brain shape prior, which is invariant to imaging parameters and ages. Experiments demonstrate that our framework can outperform the state-of-the-art methods on multi-site lifespan datasets.

Funding

National Institutes of Health (Grant No. R01CA240808 and R01CA258987)

National Natural Science Foundation of China (Grant No. U20A20386)

Shandong Provincial Natural Science Foundation (Grant No. 2022HWYQ-041)

History

Author affiliation

School of Computing and Mathematical Sciences, University of Leicester

Source

International Workshop on Machine Learning in Medical Imaging MLMI 2022: Machine Learning in Medical Imaging

Version

  • AM (Accepted Manuscript)

Published in

Lecture Notes in Computer Science

Volume

13583 LNCS

Pagination

81 - 90

Publisher

Springer Nature Switzerland

issn

0302-9743

eissn

1611-3349

isbn

9783031210136

Copyright date

2022

Available date

2023-12-16

Editors

Lian, C., Cao, X., Rekik, I., Xu, X., Cui, Z.

Book series

Lecture Notes in Computer Science

Language

en

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