Magnetic resonance imaging (MRI) has played a crucial role in fetal neurodevelopmental research. Structural annotations of MR images are an important step for quantitative analysis of the developing human brain, with Deep Learning providing an automated alternative for this otherwise tedious manual process. However, segmentation performances of Convolutional Neural Networks often suffer from domain shift, where the network fails when applied to subjects that deviate from the distribution with which it is trained on. In this work, we aim to train networks capable of automatically segmenting fetal brain MRIs with a wide range of domain shifts pertaining to differences in subject physiology and acquisition environments, in particular shape-based differences commonly observed in pathological cases. We introduce a novel data-driven train-time sampling strategy that seeks to fully exploit the diversity of a given training dataset to enhance the domain generalizability of the trained networks. We adapted our sampler, together with other existing data augmentation techniques, to the SynthSeg framework, a generator that utilizes domain randomization to generate diverse training data. We ran thorough experimentations and ablation studies on a wide range of training/testing data to test the validity of the approaches. Our networks achieved notable improvements in the segmentation quality on testing subjects with intense anatomical abnormalities (p < 1e-4), though at the cost of a slighter decrease in performance in cases with fewer abnormalities. Our work also lays the foundation for future works on creating and adapting data-driven sampling strategies for other training pipelines.

翻译：磁共振成像（MRI）在胎儿神经发育研究中发挥着至关重要的作用。对MR图像进行结构标注是量化分析发育中人脑的重要步骤，而深度学习为此项原本繁琐的手动过程提供了自动化替代方案。然而，卷积神经网络的分割性能常受域偏移影响，即当网络应用于偏离其训练数据分布的样本时会出现失效。本研究旨在训练能够自动分割具有广泛域偏移的胎儿脑部MRI的网络，这些偏移涉及受试者生理差异与采集环境差异，特别是病理病例中常见的基于形状的差异。我们提出了一种新颖的数据驱动训练时采样策略，旨在充分利用给定训练数据集的多样性，以增强所训练网络的域泛化能力。我们将该采样器与其他现有数据增强技术共同适配于SynthSeg框架——一个利用域随机化生成多样化训练数据的生成器。我们在广泛的训练/测试数据上进行了详尽的实验与消融研究以验证方法的有效性。我们的网络在对具有严重解剖异常的测试样本分割质量上取得了显著改进（p < 1e-4），尽管在异常较少的病例中性能略有下降。本研究亦为未来在其他训练流程中创建和适配数据驱动采样策略的工作奠定了基础。