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Brain Tumor Segmentation Using an Adversarial Network

  • Zeju Li
  • Yuanyuan WangEmail author
  • Jinhua YuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10670)

Abstract

Recently, the convolutional neural network (CNN) has been successfully applied to the task of brain tumor segmentation. However, the effectiveness of a CNN-based method is limited by the small receptive field, and the segmentation results don’t perform well in the spatial contiguity. Therefore, many attempts have been made to strengthen the spatial contiguity of the network output. In this paper, we proposed an adversarial training approach to train the CNN network. A discriminator network is trained along with a generator network which produces the synthetic segmentation results. The discriminator network is encouraged to discriminate the synthetic labels from the ground truth labels. Adversarial adjustments provided by the discriminator network are fed back to the generator network to help reduce the differences between the synthetic labels and the ground truth labels and reinforce the spatial contiguity with high-order loss terms. The presented method is evaluated on the Brats2017 training dataset. The experiment results demonstrate that the presented method could enhance the spatial contiguity of the segmentation results and improve the segmentation accuracy.

Keywords

Brain tumor segmentation Adversarial network Deep learning 

Notes

Acknowledgments

This work was supported by the National Basic Research Program of China (2015CB755500), the National Natural Science Foundation of China (11474071).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronic EngineeringFudan UniversityShanghaiChina
  2. 2.Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of ShanghaiShanghaiChina

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