Quantitative analysis of in utero human brain development is crucial for abnormal characterization. Magnetic resonance image (MRI) segmentation is therefore an asset for quantitative analysis. However, the development of automated segmentation methods is hampered by the scarce availability of fetal brain MRI annotated datasets and the limited variability within these cohorts. In this context, we propose to leverage the power of fetal brain MRI super-resolution (SR) reconstruction methods to generate multiple reconstructions of a single subject with different parameters, thus as an efficient tuning-free data augmentation strategy. Overall, the latter significantly improves the generalization of segmentation methods over SR pipelines.

翻译：对子宫人类大脑发育的定量分析对于异常特征分析至关重要。因此,磁共振图像分离是定量分析的一项资产,然而,由于胎儿脑部磁共振成像附加说明数据集的稀缺性和这些组群内部变化有限,自动分离方法的发展受到阻碍。在这方面,我们提议利用胎儿脑部磁共振超分辨率重建方法的力量,对具有不同参数的单一主体进行多次重建,从而成为有效的无调数据增强战略。总体而言,后者大大改进了对斯洛伐克共和国输油管的分离方法的普遍化。