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

Understand

We address the problem of segmenting 3D multi-modal medical images in scenarios where very few labeled examples are available for training.

• Leveraging the recent success of adversarial learning for semi-supervised segmentation, we propose a novel method based on Generative Adversarial Networks (GANs) to train a segmentation model with both labeled and unlabeled images.
• The proposed method prevents over-fitting by learning to discriminate between true and fake patches obtained by a generator network.
• Our work extends current adversarial learning approaches, which focus on 2D single-modality images, to the more challenging context of 3D volumes of multiple modalities.