The domain generalization problem has been widely investigated in deep learning for non-contrast imaging over the last years, but it received limited attention for contrast-enhanced imaging. However, there are marked differences in contrast imaging protocols across clinical centers, in particular in the time between contrast injection and image acquisition, while access to multi-center contrast-enhanced image data is limited compared to available datasets for non-contrast imaging. This calls for new tools for generalizing single-domain, single-center deep learning models across new unseen domains and clinical centers in contrast-enhanced imaging. In this paper, we present an exhaustive evaluation of deep learning techniques to achieve generalizability to unseen clinical centers for contrast-enhanced image segmentation. To this end, several techniques are investigated, optimized and systematically evaluated, including data augmentation, domain mixing, transfer learning and domain adaptation. To demonstrate the potential of domain generalization for contrast-enhanced imaging, the methods are evaluated for ventricular segmentation in contrast-enhanced cardiac magnetic resonance imaging (MRI). The results are obtained based on a multi-center cardiac contrast-enhanced MRI dataset acquired in four hospitals located in three countries (France, Spain and China). They show that the combination of data augmentation and transfer learning can lead to single-center models that generalize well to new clinical centers not included during training. Single-domain neural networks enriched with suitable generalization procedures can reach and even surpass the performance of multi-center, multi-vendor models in contrast-enhanced imaging, hence eliminating the need for comprehensive multi-center datasets to train generalizable models.

翻译：过去几年来,在对非高调成像的深层学习中,广泛调查了广域化问题,但在对比强化成像方面,它受到的关注有限。然而,各临床中心的对比成像规程存在明显差异,特别是在对比注射和图像获取之间的时间,而与非粘合成像的现有数据集相比,多中度对比强化成像数据的获取途径有限。这要求采用新工具,在新的隐蔽域和临床中心普及单一多度、单一中度深层学习模型,在对比强化的成像中,对对比强化成像得到的关注有限。在本文件中,我们详细评价了深度的对比成像规程技术,以达到对对比强化成像的隐蔽临床中心。为此,对多项技术进行了调查、优化和系统评估,包括数据扩增、域混合、传输学习和域适应。为了显示对比强化成像的全域化,在对比强化性心电离心电解磁再共振动成像(MRI)中,我们提出的深层次性学习技术评估方法,其结果可以建立在多级的实验中心中心,在多度上,在多级大学的实验室中,在高级研究中可以将数据转换成型模型中进行。