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Semantic segmentation constitutes an integral part of medical image analysesfor which breakthroughs in the field of deep learning were of high relevance.The large number of trainable parameters of deep neural networks howeverrenders them inherently data hungry, a characteristic that heavily challengesthe medical imaging community. Though interestingly, with the de facto standardtraining of fully convolutional networks (FCNs) for semantic segmentation beingagnostic towards the `structure' of the predicted label maps, valuablecomplementary information about the global quality of the segmentation liesidle. In order to tap into this potential, we propose utilizing an adversarialnetwork which discriminates between expert and generated annotations in orderto train FCNs for semantic segmentation. Because the adversary constitutes alearned parametrization of what makes a good segmentation at a global level, wehypothesize that the method holds particular advantages for segmentation taskson complex structured, small datasets. This holds true in our experiments: Welearn to segment aggressive prostate cancer utilizing MRI images of 152patients and show that the proposed scheme is superior over the de factostandard in terms of the detection sensitivity and the dice-score foraggressive prostate cancer. The achieved relative gains are shown to beparticularly pronounced in the small dataset limit.

Adversarial Networks for the Detection of Aggressive Prostate Cancer