  The performance of deep segmentation models often degrades due to
distribution shifts in image intensities between the training and test data
sets. This is particularly pronounced in multi-centre studies involving data
acquired using multi-vendor scanners, with variations in acquisition protocols.
It is challenging to address this degradation because the shift is often not
known \textit{a priori} and hence difficult to model. We propose a novel
framework to ensure robust segmentation in the presence of such distribution
shifts. Our contribution is three-fold. First, inspired by the spirit of
curriculum learning, we design a novel style curriculum to train the
segmentation models using an easy-to-hard mode. A style transfer model with
style fusion is employed to generate the curriculum samples. Gradually focusing
on complex and adversarial style samples can significantly boost the robustness
of the models. Second, instead of subjectively defining the curriculum
complexity, we adopt an automated gradient manipulation method to control the
hard and adversarial sample generation process. Third, we propose the Local
Gradient Sign strategy to aggregate the gradient locally and stabilise training
during gradient manipulation. The proposed framework can generalise to unknown
distribution without using any target data. Extensive experiments on the public
M\&Ms Challenge dataset demonstrate that our proposed framework can generalise
deep models well to unknown distributions and achieve significant improvements
in segmentation accuracy.
