Brain Tumor Segmentation Using Dense Fully Convolutional Neural Network

  • Mazhar Shaikh
  • Ganesh Anand
  • Gagan Acharya
  • Abhijit Amrutkar
  • Varghese Alex
  • Ganapathy KrishnamurthiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10670)


Manual segmentation of brain tumor is often time consuming and the performance of the segmentation varies based on the operators experience. This leads to the requisition of a fully automatic method for brain tumor segmentation. In this paper, we propose the usage of the 100 layer Tiramisu architecture for the segmentation of brain tumor from multi modal MR images, which is evolved by integrating a densely connected fully convolutional neural network (FCNN), followed by post-processing using a Dense Conditional Random Field (DCRF). The network consists of blocks of densely connected layers, transition down layers in down-sampling path and transition up layers in up-sampling path. The method was tested on dataset provided by Multi modal Brain Tumor Segmentation Challenge (BraTS) 2017. The training data is composed of 210 high-grade brain tumor and 74 low-grade brain tumor cases. The proposed network achieves a mean whole tumor, tumor core & active tumor dice score of 0.87, 0.68 & 0.65. Respectively on the BraTS ’17 validation set and 0.83, 0.65 & 0.65 on the Brats ’17 test set.


Fully convolutional neural networks Multi modal mri segmentation Conditional random fields Tiramisu 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Mazhar Shaikh
    • 1
  • Ganesh Anand
    • 1
  • Gagan Acharya
    • 1
  • Abhijit Amrutkar
    • 1
  • Varghese Alex
    • 1
  • Ganapathy Krishnamurthi
    • 1
    Email author
  1. 1.Medical Imaging and Reconstruction Lab, Department of Engineering DesignIndian Institute of Technology MadrasChennaiIndia

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