Brain Tumour Segmentation from Multispectral MR Image Data Using Ensemble Learning Methods

  • Ágnes Győrfi
  • Levente Kovács
  • László SzilágyiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11896)


The number of medical imaging devices is quickly and steadily rising, generating an increasing amount of image records day by day. The number of qualified human experts able to handle this data cannot follow this trend, so there is a strong need to develop reliable automatic segmentation and decision support algorithms. The Brain Tumor Segmentation Challenge (BraTS), first organized seven years ago, provoked a strong intensification of the development of brain tumor detection and segmentation algorithms. Beside many others, several ensemble learning solutions have been proposed lately to the above mentioned problem. This study presents an evaluation framework developed to evaluate the accuracy and efficiency of these algorithms deployed in brain tumor segmentation, based on the BraTS 2016 train data set. All evaluated algorithms proved suitable to provide acceptable accuracy in segmentation, but random forest was found the best, both in terms of precision and efficiency.


Magnetic resonance imaging Image segmentation Tumor detection Brain tumor Ensemble learning 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ágnes Győrfi
    • 1
    • 2
  • Levente Kovács
    • 2
  • László Szilágyi
    • 1
    • 2
    Email author
  1. 1.Computational Intelligence Research GroupSapientia - Hungarian University of TransylvaniaTîrgu MureşRomania
  2. 2.University Research, Innovation and Service Center (EKIK)Óbuda UniversityBudapestHungary

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