  Deep convolutional neural networks have proved effective in segmenting
lesions and anatomies in various medical imaging modalities. However, in the
presence of small sample size and domain shift problems, these models often
produce masks with non-intuitive segmentation mistakes. In this paper, we
propose a segmentation framework called ErrorNet, which learns to correct these
segmentation mistakes through the repeated process of injecting systematic
segmentation errors to the segmentation result based on a learned shape prior,
followed by attempting to predict the injected error. During inference,
ErrorNet corrects the segmentation mistakes by adding the predicted error map
to the initial segmentation result. ErrorNet has advantages over alternatives
based on domain adaptation or CRF-based post processing, because it requires
neither domain-specific parameter tuning nor any data from the target domains.
We have evaluated ErrorNet using five public datasets for the task of retinal
vessel segmentation. The selected datasets differ in size and patient
population, allowing us to evaluate the effectiveness of ErrorNet in handling
small sample size and domain shift problems. Our experiments demonstrate that
ErrorNet outperforms a base segmentation model, a CRF-based post processing
scheme, and a domain adaptation method, with a greater performance gain in the
presence of the aforementioned dataset limitations.
