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.
The proposed method is evaluated on the problem of segmenting brain MRI from
the iSEG-2017 and MRBrainS 2013 datasets. Significant performance improvement
is reported, compared to state-of-art segmentation networks trained in a
fully-supervised manner. In addition, our work presents a comprehensive
analysis of different GAN architectures for semi-supervised segmentation,
showing recent techniques like feature matching to yield a higher performance
than conventional adversarial training approaches. Our code is publicly
available at this https URL

我们提出了一种基于生成对抗网络（GANs）的新方法，用于使用标记和未标记的图像训练分割模型，并针对神经影像学中的 3D 多模态医学图像分割问题进行了全面分析。在对 iSEG-2017 和 MRBrainS 2013 数据集进行试验后，我们报告了与完全监督训练相比的显着性能提高。