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voxel-GAN: Adversarial Framework for Learning Imbalanced Brain Tumor Segmentation

  • Mina RezaeiEmail author
  • Haojin Yang
  • Christoph Meinel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11384)

Abstract

We propose a new adversarial network, named voxel-GAN, to mitigate imbalanced data problem in brain tumor semantic segmentation where the majority of voxels belong to a healthy region and few belong to tumor or non-health region. We introduce a 3D conditional generative adversarial network (cGAN) comprises two components: a segmentor and a discriminator. The segmentor is trained on 3D brain MR or CT images to learn the segmentation label’s in voxel-level, while the discriminator is trained to distinguish a segmentor output, coming from the ground truth or generated artificially. The segmentor and discriminator networks simultaneously train with new weighted adversarial loss to mitigate imbalanced training data issue. We show evidence that the proposed framework is applicable to different types of brain images of varied sizes. In our experiments on BraTS-2018 and ISLES-2018 benchmarks, we find improved results, demonstrating the efficacy of our approach.

Keywords

3D generative adversarial network Learning imbalanced data 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Hasso Plattner InstituteBerlinGermany

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