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Deep convolutional neural networks have proved effective in segmentinglesions and anatomies in various medical imaging modalities. However, in thepresence of small sample size and domain shift problems, these models oftenproduce masks with non-intuitive segmentation mistakes. In this paper, wepropose a segmentation framework called ErrorNet, which learns to correct thesesegmentation mistakes through the repeated process of injecting systematicsegmentation 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 mapto the initial segmentation result. ErrorNet has advantages over alternativesbased on domain adaptation or CRF-based post processing, because it requiresneither domain-specific parameter tuning nor any data from the target domains.We have evaluated ErrorNet using five public datasets for the task of retinalvessel segmentation. The selected datasets differ in size and patientpopulation, allowing us to evaluate the effectiveness of ErrorNet in handlingsmall sample size and domain shift problems. Our experiments demonstrate thatErrorNet outperforms a base segmentation model, a CRF-based post processingscheme, and a domain adaptation method, with a greater performance gain in thepresence of the aforementioned dataset limitations.

ErrorNet: Learning error representations from limited data to improve vascular segmentation