Deep Convolutional Neural Networks Using U-Net for Automatic Brain Tumor Segmentation in Multimodal MRI Volumes

  • Adel KermiEmail author
  • Issam Mahmoudi
  • Mohamed Tarek Khadir
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11384)


Precise 3D computerized segmentation of brain tumors remains, until nowadays, a challenging process due to the variety of the possible shapes, locations and image intensities of various tumors types. This paper presents a fully automated and efficient brain tumor segmentation method based on 2D Deep Convolutional Neural Networks (DNNs) which automatically extracts the whole tumor and intra-tumor regions, including enhancing tumor, edema and necrosis, from pre-operative multimodal 3D-MRI. The network architecture was inspired by U-net and has been modified to increase brain tumor segmentation performance. Among applied modifications, Weighted Cross Entropy (WCE) and Generalized Dice Loss (GDL) were employed as a loss function to address the class imbalance problem in the brain tumor data. The proposed segmentation system has been tested and evaluated on both, BraTS’2018 training and validation datasets, which include a total of 351 multimodal MRI volumes of different patients with HGG and LGG tumors representing different shapes, giving promising and objective results close to manual segmentation performances obtained by experienced neuro-radiologists. On the challenge validation dataset, our system achieved a mean enhancing tumor, whole tumor, and tumor core dice score of 0.783, 0.868 and 0.805 respectively. Other quantitative and qualitative evaluations are presented and discussed along the paper.


Brain tumor segmentation 3D-MRI Machine learning Deep learning Convolutional Neural Networks U-net BraTS’2018 challenge 



This work was in part financially supported by an Algerian research project (CNEPRU) funded by the Ministry of Higher Education and Scientific Research (Project title and number: “PERFORM”, B*04120140014). We would like to thank Bakas, S., Ph.D. and Postdoctoral researcher at SBIA of Perelman School of Medicine University of Pennsylvania – USA for providing the entire BraTS’2018 datasets employed in this study. We also gratefully acknowledge the support of CERIST, the Algerian Research Center for Scientific and Technical Information, for allowing us the use of “IBNBADIS” Cluster, without which we could not have performed these tests.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.LMCS LaboratoryNational Higher School of Computer Sciences (ESI)Oued-Smar, El-HarrachAlgeria
  2. 2.LabGed Laboratory, Department of Computer SciencesUniversity Badji-Mokhtar of AnnabaAnnabaAlgeria

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