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.
翻译:皮肤剥离是分析大脑磁共振图像(MRI)的关键先决条件步骤。 虽然提出了许多出色的工作或工具,但它们具有较低的概括性能力。例如,在具有特定成像参数的数据集方面受过训练的模型不能很好地应用于具有不同成像参数的其他数据集。特别是,对于寿命数据集,由于巨大的领域差异,在成人数据集方面受过训练的模型不适用于婴儿数据集。为了解决这一问题,已经提出了许多方法,其中基于特征校正的域适应最为常见。不幸的是,这种方法有一些内在的缺陷,需要为每个新领域重新训练,需要同时访问两个领域的输入图像。在本文件中,我们设计了一个多点和寿命头骨剥离的插件和功能形状改进框架。为了处理多点寿命数据集之间的域变,我们利用了之前的大脑形状,这种形状与成像参数和年龄不相容。实验表明,我们的框架可以超越多点寿命数据集的状态方法。