Automatic Brain Tumor Segmentation with Contour Aware Residual Network and Adversarial Training

  • Hao-Yu YangEmail author
  • Junlin Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11384)


Localizing brain tumor and identifying different subtypes of tissues play a crucial role in treatment assessment and management of gliomas. In this paper, we present a contour-aware 3D convolution neural network (CNN) with adversarial training for segmenting gliomas and an ensemble of models for overall survival prediction. For the segmentation task, contour loss and adversarial loss are added as an auxiliary information in addition to the pixel-wise classification loss to ensure the segmentation results mimic the contours of the ground truth annotation. We employed both random-forest-based and neural-network-based regression scores for predicting overall survival time. Hand-crafted imaging feature incorporated with the non-imaging feature is employed. The proposed method was evaluated on the BraTS 2018 dataset and achieved competitive results for both segmentation and survival prediction tasks. We demonstrate that raw segmentation results can be improved by incorporating extra constraints in contours and adversarial training.


Neural networks Adversarial training Contour aware 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Yale UniversityNew HavenUSA
  2. 2.Cura Cloud CooperationSeattleUSA

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