面向对比度和病理无关的临床胎儿脑MRI分割的SynthSeg方法
Towards contrast- and pathology-agnostic clinical fetal brain MRI segmentation using SynthSeg
AI总结：
本文提出了一种数据驱动的训练时采样策略，用于提升胎儿脑MRI分割的域通用性，通过SynthSeg框架实现更广泛的域偏移适应。
摘要：
磁共振成像（MRI）在胎儿神经发育研究中发挥了关键作用。结构标注MR图像是对人类发育大脑进行定量分析的重要步骤，深度学习为这一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常受到域偏移的影响，当网络应用于与训练数据分布偏离的受试者时会失效。在本工作中，我们旨在训练能够自动分割具有广泛域偏移的胎儿脑MRI的网络，特别是由于受试者生理差异和成像环境差异引起的形状差异，尤其在病理情况下常见。我们引入了一种新的数据驱动的训练时采样策略，旨在充分利用给定训练数据集的多样性，以提高训练网络的域通用性。我们将我们的采样器与其他现有的数据增强技术相结合，应用于SynthSeg框架，该框架利用域随机化生成多样化的训练数据。我们在广泛的训练/测试数据上进行了彻底的实验和消融研究，以测试方法的有效性。我们的网络在具有强烈解剖异常的测试受试者上实现了显著的分割质量提升（p < 1e-4），尽管在异常较少的情况下性能略有下降。我们的工作也为未来创建和适应其他训练流水线的数据驱动采样策略奠定了基础。
英文摘要：
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.
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