  Although numerous improvements have been made in the field of image
segmentation using convolutional neural networks, the majority of these
improvements rely on training with larger datasets, model architecture
modifications, novel loss functions, and better optimizers. In this paper, we
propose a new segmentation performance boosting paradigm that relies on
optimally modifying the network's input instead of the network itself. In
particular, we leverage the gradients of a trained segmentation network with
respect to the input to transfer it to a space where the segmentation accuracy
improves. We test the proposed method on three publicly available medical image
segmentation datasets: the ISIC 2017 Skin Lesion Segmentation dataset, the
Shenzhen Chest X-Ray dataset, and the CVC-ColonDB dataset, for which our method
achieves improvements of 5.8%, 0.5%, and 4.8% in the average Dice scores,
respectively.
