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

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