  Deep segmentation models often face the failure risks when the testing image
presents unseen distributions. Improving model robustness against these risks
is crucial for the large-scale clinical application of deep models. In this
study, inspired by human learning cycle, we propose a novel online reflective
learning framework (RefSeg) to improve segmentation robustness. Based on the
reflection-on-action conception, our RefSeg firstly drives the deep model to
take action to obtain semantic segmentation. Then, RefSeg triggers the model to
reflect itself. Because making deep models realize their segmentation failures
during testing is challenging, RefSeg synthesizes a realistic proxy image from
the semantic mask to help deep models build intuitive and effective
reflections. This proxy translates and emphasizes the segmentation flaws. By
maximizing the structural similarity between the raw input and the proxy, the
reflection-on-action loop is closed with segmentation robustness improved.
RefSeg runs in the testing phase and is general for segmentation models.
Extensive validation on three medical image segmentation tasks with a public
cardiac MR dataset and two in-house large ultrasound datasets show that our
RefSeg remarkably improves model robustness and reports state-of-the-art
performance over strong competitors.
