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We address the problem of segmenting 3D multi-modal medical images inscenarios where very few labeled examples are available for training.Leveraging the recent success of adversarial learning for semi-supervisedsegmentation, we propose a novel method based on Generative AdversarialNetworks (GANs) to train a segmentation model with both labeled and unlabeledimages. The proposed method prevents over-fitting by learning to discriminatebetween true and fake patches obtained by a generator network. Our work extendscurrent adversarial learning approaches, which focus on 2D single-modalityimages, to the more challenging context of 3D volumes of multiple modalities.The proposed method is evaluated on the problem of segmenting brain MRI fromthe iSEG-2017 and MRBrainS 2013 datasets. Significant performance improvementis reported, compared to state-of-art segmentation networks trained in afully-supervised manner. In addition, our work presents a comprehensiveanalysis of different GAN architectures for semi-supervised segmentation,showing recent techniques like feature matching to yield a higher performancethan conventional adversarial training approaches. Our code is publiclyavailable at https://github.com/arnab39/FewShot_GAN-Unet3D

Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning