Brain Tumor Segmentation and Survival Prediction Using a Cascade of Random Forests

  • Szidónia LefkovitsEmail author
  • László Szilágyi
  • László Lefkovits
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11384)


Brain tumor segmentation is a difficult task due to the strongly varying intensity and shape of gliomas. In this paper we propose a multi-stage discriminative framework for brain tumor segmentation based on BraTS 2018 dataset. The framework presented in this paper is a more complex segmentation system than our previous work presented at BraTS 2016. Here we propose a multi-stage discriminative segmentation model, where every stage is a binary classifier based on the random forest algorithm. Our multi-stage system attempts to follow the layered structure of tumor tissues provided in the annotation protocol. In each segmentation stage we dealt with four major difficulties: feature selection, determination of training database used, optimization of classifier performances and image post-processing. The framework was tested on the evaluation images from BraTS 2018. One of the most important results is the determination of the tumor ROI with a sensitivity of approximately 0.99 in stage I by considering only 16% of the brain in the subsequent stages. Based on the segmentation obtained we solved the survival prediction task using a random forest regressor. The results obtained are comparable to the best ones presented in previous BraTS Challenges.


Multi-stage classifier Random forest Feature selection Variable importance MRI brain tumor segmentation 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Medicine, Pharmacy, Sciences and TechnologyTîrgu MureşRomania
  2. 2.Department of Electrical EngineeringSapientia Hungarian University of TransylvaniaTîrgu MureşRomania

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