Using additional training data is known to improve the results, especially for medical image 3D segmentation where there is a lack of training material and the model needs to generalize well from few available data. However, the new data could have been acquired using other instruments and preprocessed such its distribution is significantly different from the original training data. Therefore, we study techniques which ameliorate domain shift during training so that the additional data becomes better usable for preprocessing and training together with the original data. Our results show that transforming the additional data using histogram matching has better results than using simple normalization.

翻译：使用额外训练数据已知能够提升结果，特别是在医学图像3D分割领域，该领域面临训练材料匮乏的问题，且模型需要从少量可用数据中实现良好的泛化能力。然而，新数据可能通过其他仪器采集并经预处理后，其数据分布与原始训练数据存在显著差异。因此，我们研究了在训练过程中缓解域偏移的技术，使额外数据能够与原始数据协同进行预处理和训练。结果表明，相较于使用简单的归一化方法，采用直方图匹配对额外数据进行变换能获得更优的效果。