  Deep learning models usually suffer from domain shift issues, where models
trained on one source domain do not generalize well to other unseen domains. In
this work, we investigate the single-source domain generalization problem:
training a deep network that is robust to unseen domains, under the condition
that training data is only available from one source domain, which is common in
medical imaging applications. We tackle this problem in the context of
cross-domain medical image segmentation. Under this scenario, domain shifts are
mainly caused by different acquisition processes. We propose a simple
causality-inspired data augmentation approach to expose a segmentation model to
synthesized domain-shifted training examples. Specifically, 1) to make the deep
model robust to discrepancies in image intensities and textures, we employ a
family of randomly-weighted shallow networks. They augment training images
using diverse appearance transformations. 2) Further we show that spurious
correlations among objects in an image are detrimental to domain robustness.
These correlations might be taken by the network as domain-specific clues for
making predictions, and they may break on unseen domains. We remove these
spurious correlations via causal intervention. This is achieved by resampling
the appearances of potentially correlated objects independently. The proposed
approach is validated on three cross-domain segmentation tasks: cross-modality
(CT-MRI) abdominal image segmentation, cross-sequence (bSSFP-LGE) cardiac MRI
segmentation, and cross-center prostate MRI segmentation. The proposed approach
yields consistent performance gains compared with competitive methods when
tested on unseen domains.
