  Automated medical image analysis has a significant value in diagnosis and
treatment of lesions. Brain tumors segmentation has a special importance and
difficulty due to the difference in appearances and shapes of the different
tumor regions in magnetic resonance images. Additionally, the data sets are
heterogeneous and usually limited in size in comparison with the computer
vision problems. The recently proposed adversarial training has shown promising
results in generative image modeling. In this paper, we propose a novel
end-to-end trainable architecture for brain tumor semantic segmentation through
conditional adversarial training. We exploit conditional Generative Adversarial
Network (cGAN) and train a semantic segmentation Convolution Neural Network
(CNN) along with an adversarial network that discriminates segmentation maps
coming from the ground truth or from the segmentation network for BraTS 2017
segmentation task[15, 4, 2, 3]. We also propose an end-to-end trainable CNN for
survival day prediction based on deep learning techniques for BraTS 2017
prediction task [15, 4, 2, 3]. The experimental results demonstrate the
superior ability of the proposed approach for both tasks. The proposed model
achieves on validation data a DICE score, Sensitivity and Specificity
respectively 0.68, 0.99 and 0.98 for the whole tumor, regarding online judgment
system.

Computer Vision and Pattern Recognition