Most publicly available brain MRI datasets are very homogeneous in terms of scanner and protocols, and it is difficult for models that learn from such data to generalize to multi-center and multi-scanner data. We propose a novel data augmentation approach with the aim of approximating the variability in terms of intensities and contrasts present in real world clinical data. We use a Gaussian Mixture Model based approach to change tissue intensities individually, producing new contrasts while preserving anatomical information. We train a deep learning model on a single scanner dataset and evaluate it on a multi-center and multi-scanner dataset. The proposed approach improves the generalization capability of the model to other scanners not present in the training data.

翻译：多数公开提供的大脑MRI数据集在扫描仪和协议方面非常均匀,从这些数据中学习的模型很难将其概括为多中层和多扫描器数据。我们建议采用新的数据增强方法,以近似真实世界临床数据中强度和对比的变异性。我们使用高山混合模型,单独改变组织强度,产生新的对比,同时保存解剖学信息。我们用单一扫描仪数据集来训练一个深学习模型,并用多中层和多扫描器数据集来评价它。拟议方法提高了模型对培训数据中未显示的其他扫描器的概括能力。