论文标题
胎儿大脑MRI分割中的域概括\\，多重构增强
Domain generalization in fetal brain MRI segmentation \\with multi-reconstruction augmentation
论文作者
论文摘要
子宫内脑发育中的定量分析对于异常表征至关重要。因此，磁共振图像（MRI）分割是用于定量分析的资产。但是，自动分割方法的发展受到胎儿大脑MRI注释数据集的稀缺性和这些同类群体中有限的可变性的阻碍。在这种情况下，我们建议利用胎儿脑MRI超分辨率（SR）重建方法的功能来生成具有不同参数的单个主题的多个重建，因此作为一种有效的无调数据增强策略。总体而言，后者显着改善了分割方法比SR管道的概括。
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.
