  Deep learning based image segmentation methods have achieved great success,
even having human-level accuracy in some applications. However, due to the
black box nature of deep learning, the best method may fail in some situations.
Thus predicting segmentation quality without ground truth would be very crucial
especially in clinical practice. Recently, people proposed to train neural
networks to estimate the quality score by regression. Although it can achieve
promising prediction accuracy, the network suffers robustness problem, e.g. it
is vulnerable to adversarial attacks. In this paper, we propose to alleviate
this problem by utilizing the difference between the input image and the
reconstructed image, which is conditioned on the segmentation to be assessed,
to lower the chance to overfit to the undesired image features from the
original input image, and thus to increase the robustness. Results on ACDC17
dataset demonstrated our method is promising.
