  Deep models often suffer from severe performance drop due to the appearance
shift in the real clinical setting. Most of the existing learning-based methods
rely on images from multiple sites/vendors or even corresponding labels.
However, collecting enough unknown data to robustly model segmentation cannot
always hold since the complex appearance shift caused by imaging factors in
daily application. In this paper, we propose a novel style-invariant method for
cardiac image segmentation. Based on the zero-shot style transfer to remove
appearance shift and test-time augmentation to explore diverse underlying
anatomy, our proposed method is effective in combating the appearance shift.
Our contribution is three-fold. First, inspired by the spirit of universal
style transfer, we develop a zero-shot stylization for content images to
generate stylized images that appearance similarity to the style images.
Second, we build up a robust cardiac segmentation model based on the U-Net
structure. Our framework mainly consists of two networks during testing: the ST
network for removing appearance shift and the segmentation network. Third, we
investigate test-time augmentation to explore transformed versions of the
stylized image for prediction and the results are merged. Notably, our proposed
framework is fully test-time adaptation. Experiment results demonstrate that
our methods are promising and generic for generalizing deep segmentation
models.
