Despite advances in data augmentation and transfer learning, convolutional neural networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans, CNNs are highly sensitive to changes in resolution and contrast: even within the same MRI modality, performance can decrease across datasets. Here we introduce SynthSeg, the first segmentation CNN robust against changes in contrast and resolution. SynthSeg is trained with synthetic data sampled from a generative model conditioned on segmentations. Crucially, we adopt a domain randomisation strategy where we fully randomise the contrast and resolution of the synthetic training data. Consequently, SynthSeg can segment real scans from a wide range of target domains without retraining or fine-tuning, which enables straightforward analysis of huge amounts of heterogeneous clinical data. Because SynthSeg only requires segmentations to be trained (no images), it can learn from labels obtained by automated methods on diverse populations (e.g., ageing and diseased), thus achieving robustness to a wide range of morphological variability. We demonstrate SynthSeg on 5,000 scans of six modalities (including CT) and ten resolutions, where it exhibits unparalleled generalisation compared with supervised CNNs, state-of-the-art domain adaptation, and Bayesian segmentation. Finally, we demonstrate the generalisability of SynthSeg by applying it to cardiac MRI and CT scans.

翻译：尽管数据增强和迁移学习取得了进展，卷积神经网络（CNN）仍难以泛化至未见过的领域。在分割脑部扫描时，CNN对分辨率和对比度的变化高度敏感：即使在同一MRI模态内，跨数据集的性能也可能下降。本文介绍SynthSeg，这是首个对对比度和分辨率变化具有鲁棒性的分割CNN。SynthSeg使用由基于分割的生成模型采样的合成数据进行训练。关键的是，我们采用域随机化策略，完全随机化合成训练数据的对比度和分辨率。因此，SynthSeg能够在无需重新训练或微调的情况下分割来自广泛目标域的实扫描，从而可直接分析海量异质性临床数据。由于SynthSeg仅需分割标签即可训练（无需图像），它可从对不同人群（如衰老与病变）的自动化方法获得的标注中学习，从而实现对广泛形态变异性的鲁棒性。我们在涵盖六种模态（包括CT）和十种分辨率的5000个扫描上验证SynthSeg，其展现出相比监督学习CNN、最先进的域自适应和贝叶斯分割无与伦比的泛化能力。最后，通过将SynthSeg应用于心脏MRI和CT扫描，我们证明了其通用性。